IGARSS 2011.ppt
•Download as PPT, PDF•
0 likes•162 views
This document summarizes a new approach for classifying remote sensing signatures extracted from multispectral imagery. It combines spectral signatures to perform accurate classification. Simulation results are provided to verify the efficiency of the proposed weighted pixel statistics approach, which uses information from multiple spectral bands. It is shown to provide more accurate and less smoothed identification of classes compared to traditional weighted order statistics methods.
Report
Share
Report
Share
![Archaeological Land Use Characterization using Multispectral Remote Sensing Data ,[object Object],[object Object],Monitoring Hidrological Variations using Multispectral SPOT-5 Data: Regional Case of Jalisco in Mexico Dr. Iván Esteban Villalón Turrubiates, Member, IEEE UNIVERSIDAD DE GUADALAJARA CENTRO UNIVERSITARIO DE LOS VALLES](https://image.slidesharecdn.com/igarss-20111-110725152642-phpapp02/85/IGARSS-2011-ppt-1-320.jpg)
![Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Scale and resolution
This document discusses concepts of scale and resolution in geography. It defines scale as the relationship between distances on a map and distances on the ground. Spatial scale involves grain, the size of pixels, and extent, the size of the study area. Resolution refers to the smallest detectable feature and depends on pixel size and the sensor's instantaneous field of view. The document outlines different types of resolution including spatial, spectral, radiometric, and temporal resolution.
Science
The document introduces an object-based approach for classifying remote sensing data to detect changes. The approach classifies groups of pixels representing objects in a GIS database, rather than single pixels. Features used in the classification include mean values of spectral bands and textures for each object. The approach was tested on two areas using object-based classifications, detecting changes in 8.6% of objects compared to the GIS database. The changes identified potential updates needed in the GIS database.
spatial resolutionin remote sensing
Spatial resolution refers to the size of the smallest detectable feature in an image. Sensors on platforms farther from their targets can image larger areas but with less detail, while closer platforms provide higher resolution images over smaller areas. Spatial resolution depends on the sensor's instantaneous field of view and pixel size, which determines the area on the ground represented by each pixel. Images with larger pixels or that can only detect larger features are considered low resolution, while high resolution images can detect smaller objects.
Advanced Remote Sensing Project Report
This document compares hyperspectral and multispectral remote sensing data for land cover analysis and classification. Hyperspectral data has higher spatial, spectral, and radiometric resolution, allowing it to more accurately classify small features, but the data sets are larger and noisier. Multispectral data has lower resolution but can still classify large landscapes effectively. A supervised classification of Florida land covers using each data type found hyperspectral data achieved 99.5% accuracy while multispectral was 91.7% accurate. However, hyperspectral misclassified some areas due to noise in its high resolution data. The best data type depends on the project scope and scale.
Congenital Bucolic and Farming Region Taxonomy Using Neural Networks for Remo...
Congenital Bucolic and Farming Region Taxonomy Using Neural Networks for Remo...DR.P.S.JAGADEESH KUMAR
This summarizes a document about classifying rural and agricultural areas using neural networks and remote sensing imagery. It discusses extracting texture features from gray scale and multispectral images to train a neural network for classification. Different features like histogram pixel intensity, texture constraints, and spatial pixel matrices were used. The neural network was able to effectively classify rural and agricultural regions by leveraging these extracted texture features from aerial images.Resolution and scanning system
Resolution and scanning system
high resolution scanning
scanning resolution for photos
high resolution scanning service
best resolution for scanning photos
best scanning resolution for documents
scanning resolution dpi
high resolution scanning near me
Chapter 5: Remote sensing
Analysis of remote sensing imagery involves identifying targets through their tone, shape, size, pattern, texture, and relationships to other objects. Targets may be environmental or artificial features appearing as points, lines, or areas. Interpretation relies on how radiation is reflected or emitted from targets and recorded by sensors to form images. The key to interpretation is recognizing targets based on these visual elements.
Remote sensing
The document discusses the four types of image resolution: spatial, spectral, radiometric, and temporal resolution. Spatial resolution refers to the ability of a sensor to identify the smallest details visible in an image. Spectral resolution is the sensor's ability to distinguish between narrow wavelength bands, while radiometric resolution refers to its ability to detect small differences in energy levels. Temporal resolution describes how frequently data is captured for a given location. Higher resolution in all four types provides more detailed, clear images for analysis.
Recommended
Scale and resolution
This document discusses concepts of scale and resolution in geography. It defines scale as the relationship between distances on a map and distances on the ground. Spatial scale involves grain, the size of pixels, and extent, the size of the study area. Resolution refers to the smallest detectable feature and depends on pixel size and the sensor's instantaneous field of view. The document outlines different types of resolution including spatial, spectral, radiometric, and temporal resolution.
Science
The document introduces an object-based approach for classifying remote sensing data to detect changes. The approach classifies groups of pixels representing objects in a GIS database, rather than single pixels. Features used in the classification include mean values of spectral bands and textures for each object. The approach was tested on two areas using object-based classifications, detecting changes in 8.6% of objects compared to the GIS database. The changes identified potential updates needed in the GIS database.
spatial resolutionin remote sensing
Spatial resolution refers to the size of the smallest detectable feature in an image. Sensors on platforms farther from their targets can image larger areas but with less detail, while closer platforms provide higher resolution images over smaller areas. Spatial resolution depends on the sensor's instantaneous field of view and pixel size, which determines the area on the ground represented by each pixel. Images with larger pixels or that can only detect larger features are considered low resolution, while high resolution images can detect smaller objects.
Advanced Remote Sensing Project Report
This document compares hyperspectral and multispectral remote sensing data for land cover analysis and classification. Hyperspectral data has higher spatial, spectral, and radiometric resolution, allowing it to more accurately classify small features, but the data sets are larger and noisier. Multispectral data has lower resolution but can still classify large landscapes effectively. A supervised classification of Florida land covers using each data type found hyperspectral data achieved 99.5% accuracy while multispectral was 91.7% accurate. However, hyperspectral misclassified some areas due to noise in its high resolution data. The best data type depends on the project scope and scale.
Congenital Bucolic and Farming Region Taxonomy Using Neural Networks for Remo...
Congenital Bucolic and Farming Region Taxonomy Using Neural Networks for Remo...DR.P.S.JAGADEESH KUMAR
This summarizes a document about classifying rural and agricultural areas using neural networks and remote sensing imagery. It discusses extracting texture features from gray scale and multispectral images to train a neural network for classification. Different features like histogram pixel intensity, texture constraints, and spatial pixel matrices were used. The neural network was able to effectively classify rural and agricultural regions by leveraging these extracted texture features from aerial images.Resolution and scanning system
Resolution and scanning system
high resolution scanning
scanning resolution for photos
high resolution scanning service
best resolution for scanning photos
best scanning resolution for documents
scanning resolution dpi
high resolution scanning near me
Chapter 5: Remote sensing
Analysis of remote sensing imagery involves identifying targets through their tone, shape, size, pattern, texture, and relationships to other objects. Targets may be environmental or artificial features appearing as points, lines, or areas. Interpretation relies on how radiation is reflected or emitted from targets and recorded by sensors to form images. The key to interpretation is recognizing targets based on these visual elements.
Remote sensing
The document discusses the four types of image resolution: spatial, spectral, radiometric, and temporal resolution. Spatial resolution refers to the ability of a sensor to identify the smallest details visible in an image. Spectral resolution is the sensor's ability to distinguish between narrow wavelength bands, while radiometric resolution refers to its ability to detect small differences in energy levels. Temporal resolution describes how frequently data is captured for a given location. Higher resolution in all four types provides more detailed, clear images for analysis.
tesi_completa
This document discusses analyzing textures in radar images for seismic damage assessment. It describes extracting texture measures from gray level co-occurrence matrices (GLCM) computed on the images. GLCMs capture the spatial relationship between pixel gray levels using parameters like window size, displacement, direction, and quantization levels. Eight common texture measures are extracted from the GLCMs, including contrast, correlation, and entropy. These measures will be analyzed using different quantization levels to optimize parameters for assessing earthquake damage levels from the radar images.
Optical Remote sensing with case studies
Optical remote sensing uses visible, near infrared, and shortwave infrared sensors to form images of the Earth's surface by detecting solar radiation reflected from targets. Different materials reflect and absorb light differently at different wavelengths, allowing targets to be differentiated in optical remote sensing images based on their spectral signatures. This document describes two case studies that used optical remote sensing to map bamboo life cycles in the Amazon and flooded areas caused by a hurricane in Texas. In both cases, indices like NDVI and NDWI that measure vegetation greenness and water content changed predictably between images, allowing researchers to accurately map phenological and flooding changes over time.
Auto Level Color Correction f or Underwater Image Matching Optimization
The absorption of the light by sea water and light scattering by small particles of underwater environment has become an obstacle of underwater vision researches with camera. It gives impact to the limitation of visibility distances camera in the sea water. The research of 3D reconstruction requires image matching technique to find out the keypoints of image pairs.
SIFT is one of the image matching technique where the quality of image matching depends on the quality of the image. This research proposed HSV conversion image with auto level color correction to increase the number of SIFT image matching. The experimental results show the number of image matching is increase until 4 %.
Classification of Radar Returns from Ionosphere Using NB-Tree and CFS
This paper present an experimental study of the different classifiers namely Naïve Bayes (NB) and NB-Tree for classification of radar returns from Ionosphere dataset. Correlation-based Feature Subset Selection (CFS) is also used for attribute selection. The purpose is to achieve the efficient result for classification. The comparison of NB classifier and NB-Tree is done based on Ionosphere dataset from UCI machine learning repository. NB-Tree classifier with CFS gives better accuracy for classification of radar returns from ionosphere. Aung Nway Oo"Classification of Radar Returns from Ionosphere Using NB-Tree and CFS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd17126.pdf http://www.ijtsrd.com/computer-science/data-miining/17126/classification-of-radar-returns-from-ionosphere-using-nb-tree-and-cfs/aung-nway-oo
PhysRevLett.105.163602
This document describes an experiment that demonstrates sub-Rayleigh resolution in imaging by using N-photon detection with a single-photon avalanche detector array. A focused laser beam is scanned over an object in an arbitrary pattern unknown to the imager. Pixels registering exactly N photon counts are used to form an image, improving resolution by a factor of (N - maximum average photon number)1/2 beyond the Rayleigh diffraction limit. The experiment images a resolution target and resolves stripes below the Rayleigh resolution with N=23 photon detection, in agreement with theory.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
This document analyzes the use of Lyapunov exponents to determine chaotic structure in hyperspectral satellite data. It investigates an EO-1 Hyperion hyperspectral image of a mixed forest site in Turkey. Lyapunov exponents are calculated from reconstructed phase spaces of spectral signals for different object classes. Positive and negative Lyapunov exponents indicate chaotic behavior is present. The results demonstrate Lyapunov exponents can be used as discriminative features to improve hyperspectral image classification accuracy by capturing the chaotic structures in the data.
An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Re...
In recent times, researchers in the remote
sensing community have been greatly interested in
utilizing hyperspectral data for in-depth analysis of
Earth’s surface. In general, hyperspectral imaging comes
with high dimensional data, which necessitates a pressing
need for efficient approaches that can effectively process
on these high dimensional data. In this paper, we present
an efficient approach for the analysis of hyperspectral
data by incorporating the concepts of Non-linear manifold
learning and k-nearest neighbor (k-NN). Instead of
dealing with the high dimensional feature space directly,
the proposed approach employs Non-linear manifold
learning that determines a low-dimensional embedding of
the original high dimensional data by computing the
geometric distances between the samples. Initially, the
dimensionality of the hyperspectral data is reduced to a
pairwise distance matrix by making use of the Johnson's
shortest path algorithm and Multidimensional scaling
(MDS). Subsequently, based on the k-nearest neighbors,
the classification of the land cover regions in the
hyperspectral data is achieved. The proposed k-NN based
approach is evaluated using the hyperspectral data
collected by the NASA’s (National Aeronautics and Space
Administration) AVIRIS (Airborne Visible/Infrared
Imaging Spectrometer) from Kennedy Space Center,
Florida. The classification accuracies of the proposed k-
NN based approach demonstrate its effectiveness in land
cover classification of hyperspectral data.
soil moisture retrieval.pptx
The document presents a method for retrieving soil moisture over bare surfaces using time-series radar observations and a lookup table representation. Key points:
1) It develops a non-empirical retrieval method that does not require ancillary data and inverts a forward model using a lookup table.
2) Monte Carlo simulations show the method can retrieve soil moisture to within 0.06 cm3/cm3 even with 17% radar measurement noise.
3) Validation with in situ data resulted in a root mean square error of 0.044 cm3/cm3 using 6-11 time-series inputs, outperforming other methods.
4) The retrieval is mostly independent of knowledge of the surface correlation length
1 IGARSS 2011 JPSS Monday Goldberg.pptx
JPSS will continue critical environmental monitoring from polar orbiting satellites by maintaining observations from sensors like CrIS, ATMS, VIIRS, OMPS, and CERES. The NPP satellite will fly the first set of these instruments in order to ensure continuity of data until JPSS-1 is launched. JPSS will provide global observations for weather forecasting and climate monitoring through environmental data records from multiple instruments measuring atmospheric, oceanic, and land surface variables. Continuity of long term data sets is essential for detecting climate change and improving weather prediction.
OverviewofGCOM.pdf
This document provides an overview of the Global Change Observation Mission (GCOM) and its first satellite, GCOM-W1. GCOM aims to continue long-term Earth observations to monitor climate change, the carbon cycle, and other variables. GCOM-W1 will carry the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument and join the A-Train constellation in 2012. AMSR2 improves on the previous AMSR-E instrument with an enhanced calibration system to provide more accurate measurements of Earth's water and energy cycles over 5 years. Future GCOM satellites will continue these essential climate observations through 2022.
IGARSS 2011.ppt
This document presents a new approach for classifying multispectral remote sensing images using weighted pixel statistics. The approach combines spectral signatures from images to perform accurate classification. Simulation results on synthesized test images show the approach provides more accurate classifications with fewer unclassified zones compared to traditional weighted order statistics methods, demonstrating the efficiency of the proposed approach. Future work is outlined to further evaluate performance.
Grammar pre study
The document discusses different types of sentences: simple sentences contain a subject and verb, and may include objects, complements or modifiers. Compound sentences join two simple sentences with a conjunction like "and". Complex sentences contain two clauses joined by conjunctions like "when" or "although". When writing sentences, proper grammar must be followed regarding subject-verb agreement, avoiding fragments, using correct parts of speech and tense. Commas alone cannot join sentences; a conjunction is needed to avoid a comma splice error.
Model-based Polarimetric Decomposition using PolInSAR Coherence_v11(FILEminim...
This document presents a new model-based polarimetric decomposition technique that utilizes polarimetric synthetic aperture radar interferometry (PolInSAR) coherence. Current decomposition models have limitations in discriminating certain scattering mechanisms like skew-oriented buildings. The proposed method adapts the volume scattering model using PolInSAR coherence, which is sensitive to diverse terrains. It also indirectly modifies double-bounce and single-bounce models. Experiments on PolInSAR data show the new technique better discriminates scattering mechanisms like buildings and fits diverse forest terrains compared to existing methods.
Igarss-Slides-2011-Fung.ppt
This document summarizes key points to consider when selecting a microwave scattering or emission model for interpreting remote sensing data of geophysical surfaces. It notes that effective model parameters may not match actual surface properties, and that natural surfaces often have multiple roughness scales. Scattering is more sensitive to surface correlation than height statistics. Both surface and volume scattering can contribute, with volume scattering dominating at large incidence angles. Emission is affected by boundary porosity and dielectric properties. Ignoring surface features like loose dirt can lead to modeling errors. Proper model selection requires accurately representing the modeled surface characteristics.
Tjuatja_IGARSS-11_prez.pptx
This study developed a physics-based model to simulate microwave backscattering from the sea surface under precipitation. The model accounts for the vertical profile of rain parameters within the column. It models the sea surface, rain layer with 12 sublayers, rain rate, drop size distribution, and other factors. Model analyses show the effects of precipitation are negligible at C-band but detectable at Ku-band based on angular trends and separation between VV and HH polarizations. The model was qualitatively validated and can provide quantitative links between geophysical parameters and backscattering measurements.
WE1.L10 - GRACE Applications to Regional Hydrology and Water Resources
This document summarizes the applications of NASA's GRACE mission for monitoring regional hydrology and water resources. GRACE uses two satellites to measure small changes in Earth's gravity field caused by the redistribution of water on land and oceans. GRACE data has been used to monitor seasonal water storage changes, depleting groundwater aquifers, declining glaciers and ice sheets, and rising sea levels. Ensuring continuity of GRACE measurements is important for long-term climate monitoring, and NASA has proposed a GRACE Follow-On mission to launch in 2016 to fill the gap until next-generation gravity missions.
simplex.pdf
The document proposes a hyperspectral unmixing method called Simplex Volume Analysis based on Triangular Factorization (SVATF). SVATF extracts endmembers by maximizing the volume of a simplex formed by the endmember spectra. It calculates the simplex volume using Cholesky factorization on a smaller matrix for faster computation compared to other methods. SVATF then estimates abundance fractions by solving a system of linear equations using the extracted endmembers. The method is evaluated on synthetic and real hyperspectral data and shows improved computational efficiency over other common endmember extraction algorithms.
Fr1T101-Kuo-20110729 IGARSS ESC.pptx
The document proposes an Earth Science Collaboratory (ESC) that would provide access to Earth science models, data, tools, and services to facilitate collaboration and reproducibility in data-intensive Earth science research. It describes the current fragmented state of accessing and sharing models, data, tools, and knowledge. The ESC would integrate these components and provide services like cloud computing, discovery, and provenance tracking. It presents a use case of how the ESC could help collaboration in the development of precipitation retrieval algorithms for the Global Precipitation Measurement mission.
WE1.L10 - INTERANNUAL VARIABILITY IN CLIMATE PRODUCTS FROM THE ATMOSPHERIC IN...
The document summarizes the Atmospheric Infrared Sounder (AIRS) instrument on NASA's Aqua satellite. AIRS measures atmospheric temperature and humidity profiles as well as greenhouse gases. It has provided over 8 years of global observations used to improve weather forecasts and validate climate models. AIRS data has also enabled over 350 research publications studying topics like tropical cyclones, the Madden-Julian oscillation, and trends in outgoing longwave radiation.
POLAND OUR COUNTRY Comenius meeting XI 2011
Comenius 2011-2013, project: An extra place at table or know each other through food, The first meeting in Poland, Chrzanów, XI 2011, Primary School nr 8 in Chrzanów, Coordinator: Bernadetta Utzig
Argumentative
This document provides guidance on how to effectively argue a point in response to another person's opinion. It recommends focusing the argument by using sound reasons, supporting evidence, statistics, facts, and expert quotes. The argument should be organized with an introduction stating the other person's opinion and your intention to disagree, followed by multiple arguments and a final thought. Each argument should present a clear point supported by logical reasoning and evidence. The conclusion should firmly restate your viewpoint without introducing new ideas.
More Related Content
What's hot
tesi_completa
This document discusses analyzing textures in radar images for seismic damage assessment. It describes extracting texture measures from gray level co-occurrence matrices (GLCM) computed on the images. GLCMs capture the spatial relationship between pixel gray levels using parameters like window size, displacement, direction, and quantization levels. Eight common texture measures are extracted from the GLCMs, including contrast, correlation, and entropy. These measures will be analyzed using different quantization levels to optimize parameters for assessing earthquake damage levels from the radar images.
Optical Remote sensing with case studies
Optical remote sensing uses visible, near infrared, and shortwave infrared sensors to form images of the Earth's surface by detecting solar radiation reflected from targets. Different materials reflect and absorb light differently at different wavelengths, allowing targets to be differentiated in optical remote sensing images based on their spectral signatures. This document describes two case studies that used optical remote sensing to map bamboo life cycles in the Amazon and flooded areas caused by a hurricane in Texas. In both cases, indices like NDVI and NDWI that measure vegetation greenness and water content changed predictably between images, allowing researchers to accurately map phenological and flooding changes over time.
Auto Level Color Correction f or Underwater Image Matching Optimization
The absorption of the light by sea water and light scattering by small particles of underwater environment has become an obstacle of underwater vision researches with camera. It gives impact to the limitation of visibility distances camera in the sea water. The research of 3D reconstruction requires image matching technique to find out the keypoints of image pairs.
SIFT is one of the image matching technique where the quality of image matching depends on the quality of the image. This research proposed HSV conversion image with auto level color correction to increase the number of SIFT image matching. The experimental results show the number of image matching is increase until 4 %.
Classification of Radar Returns from Ionosphere Using NB-Tree and CFS
This paper present an experimental study of the different classifiers namely Naïve Bayes (NB) and NB-Tree for classification of radar returns from Ionosphere dataset. Correlation-based Feature Subset Selection (CFS) is also used for attribute selection. The purpose is to achieve the efficient result for classification. The comparison of NB classifier and NB-Tree is done based on Ionosphere dataset from UCI machine learning repository. NB-Tree classifier with CFS gives better accuracy for classification of radar returns from ionosphere. Aung Nway Oo"Classification of Radar Returns from Ionosphere Using NB-Tree and CFS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd17126.pdf http://www.ijtsrd.com/computer-science/data-miining/17126/classification-of-radar-returns-from-ionosphere-using-nb-tree-and-cfs/aung-nway-oo
PhysRevLett.105.163602
This document describes an experiment that demonstrates sub-Rayleigh resolution in imaging by using N-photon detection with a single-photon avalanche detector array. A focused laser beam is scanned over an object in an arbitrary pattern unknown to the imager. Pixels registering exactly N photon counts are used to form an image, improving resolution by a factor of (N - maximum average photon number)1/2 beyond the Rayleigh diffraction limit. The experiment images a resolution target and resolves stripes below the Rayleigh resolution with N=23 photon detection, in agreement with theory.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
This document analyzes the use of Lyapunov exponents to determine chaotic structure in hyperspectral satellite data. It investigates an EO-1 Hyperion hyperspectral image of a mixed forest site in Turkey. Lyapunov exponents are calculated from reconstructed phase spaces of spectral signals for different object classes. Positive and negative Lyapunov exponents indicate chaotic behavior is present. The results demonstrate Lyapunov exponents can be used as discriminative features to improve hyperspectral image classification accuracy by capturing the chaotic structures in the data.
An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Re...
In recent times, researchers in the remote
sensing community have been greatly interested in
utilizing hyperspectral data for in-depth analysis of
Earth’s surface. In general, hyperspectral imaging comes
with high dimensional data, which necessitates a pressing
need for efficient approaches that can effectively process
on these high dimensional data. In this paper, we present
an efficient approach for the analysis of hyperspectral
data by incorporating the concepts of Non-linear manifold
learning and k-nearest neighbor (k-NN). Instead of
dealing with the high dimensional feature space directly,
the proposed approach employs Non-linear manifold
learning that determines a low-dimensional embedding of
the original high dimensional data by computing the
geometric distances between the samples. Initially, the
dimensionality of the hyperspectral data is reduced to a
pairwise distance matrix by making use of the Johnson's
shortest path algorithm and Multidimensional scaling
(MDS). Subsequently, based on the k-nearest neighbors,
the classification of the land cover regions in the
hyperspectral data is achieved. The proposed k-NN based
approach is evaluated using the hyperspectral data
collected by the NASA’s (National Aeronautics and Space
Administration) AVIRIS (Airborne Visible/Infrared
Imaging Spectrometer) from Kennedy Space Center,
Florida. The classification accuracies of the proposed k-
NN based approach demonstrate its effectiveness in land
cover classification of hyperspectral data.
What's hot (7)
Auto Level Color Correction f or Underwater Image Matching Optimization
Auto Level Color Correction f or Underwater Image Matching Optimization
Classification of Radar Returns from Ionosphere Using NB-Tree and CFS
Classification of Radar Returns from Ionosphere Using NB-Tree and CFS
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Re...
An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Re...
Viewers also liked
soil moisture retrieval.pptx
The document presents a method for retrieving soil moisture over bare surfaces using time-series radar observations and a lookup table representation. Key points:
1) It develops a non-empirical retrieval method that does not require ancillary data and inverts a forward model using a lookup table.
2) Monte Carlo simulations show the method can retrieve soil moisture to within 0.06 cm3/cm3 even with 17% radar measurement noise.
3) Validation with in situ data resulted in a root mean square error of 0.044 cm3/cm3 using 6-11 time-series inputs, outperforming other methods.
4) The retrieval is mostly independent of knowledge of the surface correlation length
1 IGARSS 2011 JPSS Monday Goldberg.pptx
JPSS will continue critical environmental monitoring from polar orbiting satellites by maintaining observations from sensors like CrIS, ATMS, VIIRS, OMPS, and CERES. The NPP satellite will fly the first set of these instruments in order to ensure continuity of data until JPSS-1 is launched. JPSS will provide global observations for weather forecasting and climate monitoring through environmental data records from multiple instruments measuring atmospheric, oceanic, and land surface variables. Continuity of long term data sets is essential for detecting climate change and improving weather prediction.
OverviewofGCOM.pdf
This document provides an overview of the Global Change Observation Mission (GCOM) and its first satellite, GCOM-W1. GCOM aims to continue long-term Earth observations to monitor climate change, the carbon cycle, and other variables. GCOM-W1 will carry the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument and join the A-Train constellation in 2012. AMSR2 improves on the previous AMSR-E instrument with an enhanced calibration system to provide more accurate measurements of Earth's water and energy cycles over 5 years. Future GCOM satellites will continue these essential climate observations through 2022.
IGARSS 2011.ppt
This document presents a new approach for classifying multispectral remote sensing images using weighted pixel statistics. The approach combines spectral signatures from images to perform accurate classification. Simulation results on synthesized test images show the approach provides more accurate classifications with fewer unclassified zones compared to traditional weighted order statistics methods, demonstrating the efficiency of the proposed approach. Future work is outlined to further evaluate performance.
Grammar pre study
The document discusses different types of sentences: simple sentences contain a subject and verb, and may include objects, complements or modifiers. Compound sentences join two simple sentences with a conjunction like "and". Complex sentences contain two clauses joined by conjunctions like "when" or "although". When writing sentences, proper grammar must be followed regarding subject-verb agreement, avoiding fragments, using correct parts of speech and tense. Commas alone cannot join sentences; a conjunction is needed to avoid a comma splice error.
Model-based Polarimetric Decomposition using PolInSAR Coherence_v11(FILEminim...
This document presents a new model-based polarimetric decomposition technique that utilizes polarimetric synthetic aperture radar interferometry (PolInSAR) coherence. Current decomposition models have limitations in discriminating certain scattering mechanisms like skew-oriented buildings. The proposed method adapts the volume scattering model using PolInSAR coherence, which is sensitive to diverse terrains. It also indirectly modifies double-bounce and single-bounce models. Experiments on PolInSAR data show the new technique better discriminates scattering mechanisms like buildings and fits diverse forest terrains compared to existing methods.
Igarss-Slides-2011-Fung.ppt
This document summarizes key points to consider when selecting a microwave scattering or emission model for interpreting remote sensing data of geophysical surfaces. It notes that effective model parameters may not match actual surface properties, and that natural surfaces often have multiple roughness scales. Scattering is more sensitive to surface correlation than height statistics. Both surface and volume scattering can contribute, with volume scattering dominating at large incidence angles. Emission is affected by boundary porosity and dielectric properties. Ignoring surface features like loose dirt can lead to modeling errors. Proper model selection requires accurately representing the modeled surface characteristics.
Tjuatja_IGARSS-11_prez.pptx
This study developed a physics-based model to simulate microwave backscattering from the sea surface under precipitation. The model accounts for the vertical profile of rain parameters within the column. It models the sea surface, rain layer with 12 sublayers, rain rate, drop size distribution, and other factors. Model analyses show the effects of precipitation are negligible at C-band but detectable at Ku-band based on angular trends and separation between VV and HH polarizations. The model was qualitatively validated and can provide quantitative links between geophysical parameters and backscattering measurements.
WE1.L10 - GRACE Applications to Regional Hydrology and Water Resources
This document summarizes the applications of NASA's GRACE mission for monitoring regional hydrology and water resources. GRACE uses two satellites to measure small changes in Earth's gravity field caused by the redistribution of water on land and oceans. GRACE data has been used to monitor seasonal water storage changes, depleting groundwater aquifers, declining glaciers and ice sheets, and rising sea levels. Ensuring continuity of GRACE measurements is important for long-term climate monitoring, and NASA has proposed a GRACE Follow-On mission to launch in 2016 to fill the gap until next-generation gravity missions.
simplex.pdf
The document proposes a hyperspectral unmixing method called Simplex Volume Analysis based on Triangular Factorization (SVATF). SVATF extracts endmembers by maximizing the volume of a simplex formed by the endmember spectra. It calculates the simplex volume using Cholesky factorization on a smaller matrix for faster computation compared to other methods. SVATF then estimates abundance fractions by solving a system of linear equations using the extracted endmembers. The method is evaluated on synthetic and real hyperspectral data and shows improved computational efficiency over other common endmember extraction algorithms.
Fr1T101-Kuo-20110729 IGARSS ESC.pptx
The document proposes an Earth Science Collaboratory (ESC) that would provide access to Earth science models, data, tools, and services to facilitate collaboration and reproducibility in data-intensive Earth science research. It describes the current fragmented state of accessing and sharing models, data, tools, and knowledge. The ESC would integrate these components and provide services like cloud computing, discovery, and provenance tracking. It presents a use case of how the ESC could help collaboration in the development of precipitation retrieval algorithms for the Global Precipitation Measurement mission.
WE1.L10 - INTERANNUAL VARIABILITY IN CLIMATE PRODUCTS FROM THE ATMOSPHERIC IN...
The document summarizes the Atmospheric Infrared Sounder (AIRS) instrument on NASA's Aqua satellite. AIRS measures atmospheric temperature and humidity profiles as well as greenhouse gases. It has provided over 8 years of global observations used to improve weather forecasts and validate climate models. AIRS data has also enabled over 350 research publications studying topics like tropical cyclones, the Madden-Julian oscillation, and trends in outgoing longwave radiation.
POLAND OUR COUNTRY Comenius meeting XI 2011
Comenius 2011-2013, project: An extra place at table or know each other through food, The first meeting in Poland, Chrzanów, XI 2011, Primary School nr 8 in Chrzanów, Coordinator: Bernadetta Utzig
Argumentative
This document provides guidance on how to effectively argue a point in response to another person's opinion. It recommends focusing the argument by using sound reasons, supporting evidence, statistics, facts, and expert quotes. The argument should be organized with an introduction stating the other person's opinion and your intention to disagree, followed by multiple arguments and a final thought. Each argument should present a clear point supported by logical reasoning and evidence. The conclusion should firmly restate your viewpoint without introducing new ideas.
Metropolitan architects
This document discusses the history of urban planning from the industrial era to the present. It outlines different eras and experiments in urban planning such as industrial cities in the 1800s, garden cities in the early 1900s, new towns in the mid-1900s, and suburban development. It also mentions failed visions, bespoke visions in Dubai and Shanghai, and the importance of context, culture and climate in urban planning. The document examines layers of history and influences on urban planning over time.
Formont.ppt
1. The document discusses unsupervised classification of polarimetric synthetic aperture radar (SAR) data using information geometry of covariance matrices.
2. It presents the spherical and independently and identically distributed (SIRV) model for non-Gaussian clutter and proposes a maximum likelihood estimator of covariance matrices under the SIRV assumption.
3. Results on real SAR data show that accounting for the non-Gaussian nature of the data using the SIRV model and a Riemannian geometric mean of covariances outperforms conventional Gaussian methods.
RoughSurfaceModels.ppt
The document summarizes recent advances in analytical models for electromagnetic scattering from rough surfaces, including the statistical integral equation method (SIEM) and the extended advanced integral equation method (E-AIEM). SIEM incorporates surface slope statistics into the Kirchhoff approximation to improve accuracy. E-AIEM makes fewer assumptions than previous models and includes correction terms involving the error function for more accurate cross and complementary scattering coefficients, especially when surfaces or media are lossy. Testing shows these new methods agree well with full-wave simulations and experimental data.
TU1.T10.5.ppt
The document presents a new technique called C-2PO for retrieving wind speeds from cross-polarized synthetic aperture radar (SAR) data during hurricanes. C-2PO uses the cross-polarization ratio, which is insensitive to wind direction and incidence angle, making it easier to map to wind speed compared to existing techniques. Comparisons show C-2PO provides more accurate wind speed retrievals than existing methods for hurricanes, with lower biases and errors. Reasons C-2PO performs better include existing models saturating at high winds and not accounting for rain and other factors.
Cultural / Civic Entrepreneurs Forum: NSPA
The Cultural / Civic Entrepreneurs Forum is aimed at highlighting initiatives that have the potential to stimulate and enliven the public and community spaces of the city. Great places are not just an outcome of design but management , programming & Community Engagement & enabling social networks & partnerships. The forum presented cultural change agents who organize and adding to the cultural & social capital of the city. It was jointly hosted by The Urban Vision & Thomson Reuters Foundation. Below is the presentations.
Viewers also liked (20)
Model-based Polarimetric Decomposition using PolInSAR Coherence_v11(FILEminim...
Model-based Polarimetric Decomposition using PolInSAR Coherence_v11(FILEminim...
WE1.L10 - GRACE Applications to Regional Hydrology and Water Resources
WE1.L10 - GRACE Applications to Regional Hydrology and Water Resources
WE1.L10 - INTERANNUAL VARIABILITY IN CLIMATE PRODUCTS FROM THE ATMOSPHERIC IN...
WE1.L10 - INTERANNUAL VARIABILITY IN CLIMATE PRODUCTS FROM THE ATMOSPHERIC IN...
Similar to IGARSS 2011.ppt
Super-Resolution of Multispectral Images
This document summarizes a proposed method for super-resolution of multispectral images using principal component analysis. It begins with background on multispectral imaging and issues with resolution. The proposed method first uses PCA to reduce the dimensionality of the multispectral data. It then learns edge details from a high-resolution database by matching blocks of the principal components. After learning, the modified principal components are inverse transformed to generate a higher resolution multispectral image. The method is tested on real multispectral data sets and shown to reconstruct higher resolution images.
Digital image classification22oct
Digital image classification is the process of sorting pixels into categories based on their spectral values. There are supervised and unsupervised classification methods. Supervised classification involves using training sites of known categories to define statistical signatures for each class. Unsupervised classification groups pixels into clusters without prior class definitions. Validation is needed to assess classification accuracy by comparing results to ground truth data. Factors like training site selection and signature separability impact classification performance.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
Fd36957962
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Digital_Image_Classification.pptx
This document discusses digital image classification and accuracy assessment. It covers topics such as spectral signatures, supervised vs. unsupervised classification, object-based image analysis, and accuracy assessment methods. The key points are:
- Digital image classification uses spectral information from pixels to categorize land cover types based on spectral patterns. Both supervised and unsupervised methods are described.
- Supervised classification uses training samples of known identity to classify pixels, while unsupervised classification uses computer clustering to group spectrally similar pixels without training data.
- Object-based image analysis first segments an image into meaningful image objects before classification, allowing use of texture, shape, and context versus just spectral values.
- Accuracy assessment requires reference data
Digital image processing
Digital image processing ,Techniques of Digital Image Processing
Image Enhancement ,Image Histogram,Spatial Filtering,Smoothing and Sharpening Examples,Image Classification,Supervised Classification,Unsupervised Classification
remote sensing applications.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.Digital Image Classification.pptx
Digital image classification involves sorting pixels into discrete classes based on their spectral values. It can be performed using supervised or unsupervised approaches. Supervised classification involves using training data to define classes, while unsupervised classification uses algorithms to automatically group similar pixels. Accuracy assessment involves comparing the classification to reference data to determine accuracy through an error matrix.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
This document summarizes a research paper on using the Cholesky decomposition technique to fuse multispectral images and represent them as a color image. It discusses how multispectral image fusion works by combining images from different spectral bands. It then describes the VTVA (Vector valued Total Variation Algorithm) technique in detail, which uses the covariance matrix and Cholesky decomposition to control the correlation between color components in the fused image. This technique is compared to principal component analysis. The document provides background on RGB color space, color perception, and Cholesky decomposition before outlining the specific steps of the VTVA algorithm.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of
image fusion algorithms that would combine the image from these sensors in an efficient way to give an
image that is more perceptible to human eye. Multispectral Image fusion is the process of combining
images optically acquired in more than one spectral band. In this paper, we present a pixel-level image
fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um),
mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a
composite colour image. The work coalesces a fusion technique that involves linear transformation based
on Cholesky decomposition of the covariance matrix of source data that converts multispectral source
images which are in grayscale into colour image. This work is composed of different segments that
includes estimation of covariance matrix of images, cholesky decomposition and transformation ones.
Finally, the fused colour image is compared with the fused image obtained by PCA transformation.
Satellite Image Classification using Decision Tree, SVM and k-Nearest Neighbor
Satellite Image Classification using Decision Tree, SVM and k-Nearest NeighborNational Cheng Kung University
This document summarizes research comparing three machine learning classification methods - Decision Tree, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN) - for classifying land use from high and low resolution satellite imagery. The researchers applied each method to classify Pleiades satellite images of Taiwan and Colorado. SVM achieved the highest overall accuracy of 78.6% for high resolution imagery and 83.3% for low resolution imagery. Decision Trees and k-NN were less accurate. The document outlines the methodology, including image preprocessing, parameter selection, accuracy assessment, and findings.P.maria sheeba 15 mco010
This document discusses image segmentation techniques for hyperspectral images. It first defines image segmentation as extracting particular regions from images, and lists common segmentation methods like edge detection, contour extraction, and clustering. The objective is then stated as improving noise-free estimation for noisy hyperspectral images by fully utilizing correlated spectral information and providing rich spectral-spatial information. Finally, the document surveys several papers on hyperspectral image denoising and restoration, discussing their purposes, methodologies, and limitations.
Lw3620362041
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Detection of urban tree canopy from very high resolution imagery using an ob...
Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.
IMAGE QUALITY OPTIMIZATION USING RSATV
Abstract: Primarily due to the progresses in super resolution imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a image segmentation based on colour features with K-means clustering. The entire work is divided into two stages. First enhancement of color separation of satellite image using de correlation stretching is carried out and then the regions are grouped into a set of five classes using K-means clustering algorithm. At first, the spatial data is concentrated focused around every pixel, and at that point two separating procedures are added to smother the impact of pseudoedges. What's more, the spatial data weight is built and grouped with k-means bunching, and the regularization quality in every district is controlled by the bunching focus esteem. The exploratory results, on both reenacted and genuine datasets, demonstrate that the proposed methodology can adequately lessen the pseudoedges of the aggregate variety regularization in the level.
Basics of remote sensing and GIS.pptx
This document provides an overview of remote sensing and describes its key principles and applications. It defines remote sensing as acquiring information about planetary surfaces from a distance without direct contact. The main components of a remote sensing system are described as the energy source, atmosphere, target interaction, sensor recording, transmission and processing, interpretation and analysis, and applications. Common data types like raster and vector data are also explained. Remote sensing techniques like digital image processing, classification, and analysis are outlined. Examples of satellite imagery and classifications are provided.
Mn3621372142
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR M...
Remote sensing is collecting information about an object without any direct physical contact with the particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing uses the Satellite to detect and classify the particular object or area. They also classify the object on the earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the classifiers of previous images to decrease the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is predicted first based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate manner with current training samples. This approach can be further applied as sequential image data, with only a small number of training samples, which are being required from each image. This method uses LANSAT 8 images for Training and Testing processes. First, using the Classifier Prediction technique the Signatures are being generated for the input images. The generated Signatures are used for the Training purposes. SVM Classification is used for classifying the images. The final results describes that the leverage of a priori information from previous images will provide advantageous improvement for future images in multi temporal image classification.
RADIOMETRIC RESOLUTION.pptx
Radiometric resolution refers to a sensor's ability to distinguish slight differences in energy levels and is measured in bits. More bits allows an image to store more grayscale values, enabling better discrimination between classes. For example, Landsat-8 has 12-bit resolution, allowing it to distinguish over 4,000 grayscale levels and better characterize land cover compared to Landsat-7's 8-bit resolution of 256 levels. Higher radiometric resolution improves a sensor's sensitivity to capture subtle differences in surface reflectance.
Shadow Detection and Removal Techniques A Perspective View
This document discusses techniques for shadow detection and removal in images. It provides an overview of various methods used, including those based on texture analysis, color information, Gaussian mixture models, and deterministic non-model based approaches. The document then reviews several published papers on different shadow detection and removal algorithms. These algorithms are compared based on advantages and disadvantages in terms of accuracy, computational efficiency, and applicability to different image types and conditions. The conclusion is that shadows remain a challenging problem for computer vision tasks and that the most suitable detection and removal technique depends on the specific image type and application.
Similar to IGARSS 2011.ppt (20)
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION
Satellite Image Classification using Decision Tree, SVM and k-Nearest Neighbor
Satellite Image Classification using Decision Tree, SVM and k-Nearest Neighbor
Detection of urban tree canopy from very high resolution imagery using an ob...
Detection of urban tree canopy from very high resolution imagery using an ob...
CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR M...
CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR M...
Shadow Detection and Removal Techniques A Perspective View
Shadow Detection and Removal Techniques A Perspective View
More from grssieee
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
1) The document describes a segmentation algorithm for polarimetric SAR (PolSAR) data that can model both scalar-texture and multi-texture scattering.
2) The algorithm uses log-cumulants and hypothesis testing to determine whether a scalar-texture or dual-texture model best fits the data within each segment.
3) The algorithm is tested on simulated multi-texture PolSAR data and is shown to accurately segment the classes and estimate their texture parameters. However, when applied to real data sets, the algorithm only finds the simpler scalar-texture case.
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
This document discusses using wavelet transforms to analyze two-point statistics of polarimetric synthetic aperture radar (PolSAR) data. It introduces wavelet variance and kurtosis as metrics that can be applied to PolSAR data transformed using a wavelet frame. It then provides an example of applying this analysis to ALOS PALSAR data over Hawaii's Papau Seamount to characterize sea surface features.
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
The Sentinel-1 mission is part of the GMES program and consists of two satellites to provide C-band SAR data for emergency response, marine and land monitoring, and other applications. The satellites operate in a near-polar orbit with a 12 day repeat cycle. The main acquisition mode is an interferometric wide swath mode with 5m range and 20m azimuth resolution over a 250km swath. Sentinel-1 will support operational services and create a long-term SAR data archive.
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
The document summarizes the status of the GMES Space Component program. It describes the Sentinel satellite missions for monitoring land, ocean, atmosphere and emergency situations. The Sentinels will provide long-term data continuity as well as improved coverage compared to existing missions. Sentinel data will be freely and openly available to both operational users and the science community. The program is on track, with the first Sentinel launches beginning in 2013.
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
The document describes the progress of the development of CFOSAT SCAT, a Ku-band scatterometer onboard the Chinese-French Oceanography Satellite (CFOSAT). CFOSAT will measure global ocean surface winds and waves to improve weather forecasting, ocean dynamics modeling, climate research, and understanding of surface processes. The SCAT instrument is a rotating fan-beam radar scatterometer that will retrieve wind vectors using measurements of backscatter at incidence angles from 26 to 46 degrees. It has a wide swath of over 1000km and specifications are designed to achieve high-precision wind measurements globally. System details including parameters and the operation mode are provided.
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
The document describes the SAP4PRISMA project which aims to develop algorithms and products to support the Italian hyperspectral PRISMA Earth observation mission. The project will focus on data processing, quality assessment, classification methods, and generating level 3 and 4 products for applications like land monitoring, agriculture, and hazard monitoring. It will include the generation of "PRISMA-like" synthetic test data to support algorithm development and validation. The research will be carried out across multiple work packages focusing on topics like data quality, classification methods, calibration/validation, and developing applicative products.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different sites, including a Zambian woodland and North Carolina forest sites.
3) Time series of Hyperion data at flux tower sites show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different forest, grassland, and woodland sites globally.
3) Time series of Hyperion data at sites in Zambia, North Carolina, and Kansas show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release measured by flux towers.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/Hyperion has been collecting hyperspectral imagery for over 12 years, acquiring over 65,000 scenes. Researchers have been using these data to develop and validate algorithms for estimating vegetation properties like fraction of absorbed photosynthetically active radiation (fAPAR) and photochemical reflectance index (PRI). Comparisons of Hyperion data to field measurements at flux tower sites show these algorithms can accurately track vegetation changes over time and relate spectral properties to productivity metrics like light use efficiency and gross ecosystem productivity. This work is helping prototype data products for the upcoming HyspIRI mission.
Test
This document is a return and exchange form for a wetsuit company. It provides instructions for customers to fill out when returning an undamaged item for a refund, exchange, or size change. The form requests information like the customer's order details, contact information, the suit being returned and its size, the reason for return, and if applicable, the new desired size. It also provides the return shipping address and notifies customers that the company is not responsible for lost or damaged return packages.
2011_Fox_Tax_Worksheets.pdf
This document provides instructions for clients of Fox Tax Planning and Preparation for preparing to have their taxes filed. It lists important income and deduction documentation to bring to an appointment, such as W-2s, 1099s, receipts for donations. It also includes an engagement letter detailing the services to be provided, responsibilities of both parties, fees, and electronic filing and signature procedures. Clients are asked to sign the letter agreeing to the terms and return it along with their tax information.
Tana_IGARSS2011.ppt
The document discusses mapping wetlands in North America using MODIS 500m imagery. It describes wetlands and existing global wetland databases. The methodology uses MODIS data from 2008, digital elevation models, and reference data to classify wetlands into three types - forest/shrub dominant wetlands, herbaceous dominant wetlands, and sea grass dominant wetlands. Training data is collected from existing land cover maps and Landsat imagery. A decision tree model and maximum likelihood classification are applied to extract wetlands from other land covers.
Solaro_IGARSS_2011.ppt
The document summarizes research using SBAS-DInSAR (Small BAseline Subset differential interferometric synthetic aperture radar) techniques to analyze ground deformation at Mt. Etna volcano in Italy over the last 18 years using ERS and ENVISAT satellite data. The analysis revealed three main deformation processes: inflation of the volcanic edifice, subsidence of sectors on the eastern flank due to gravitational spreading, and deflation-inflation cycles associated with eruptive and post-eruptive activity. More recent analysis using higher resolution COSMO-SkyMed data from 2009-2010 detected deformation related to faults and a 2010 earthquake more precisely than lower resolution ENVISAT data.
More from grssieee (20)
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
Recently uploaded
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Recently uploaded (20)
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
IGARSS 2011.ppt
- 10. A) Illumination Source B) Radiation C) Interaction with the object D) Radiation sensing E) Transmission, reception and data processing F) Analysis and interpretation G) Application Process
- 15. Temporal Resolution Time July 1 July 12 July 23 August 3 11 days 16 days July 2 July 8 August 3
- 16. Radiometric Resolution 6-bits Range 0 63 8-bits Range 0 255 0 10-bits Range 1023
- 17. INTRODUCTION TO IMAGE CLASSIFICATION
- 24. Basic Strategy: How to do it? But in reality, that is not the case. Looking at several pixels with vegetation, you’d see variety in spectral signatures. The same would happen for other types of pixels, as well.
- 26. Think of a pixel’s brightness in a 2-Dimensional space. The pixel occupies a point in that space. The vegetation pixel and the soil pixel occupy different points in a 2-D space.
- 27. With variability, the vegetation pixels now occupy a region, not a point, of n-Dimensional space. Soil pixels occupy a different region of n-Dimensional space.
- 30. Supervised Classification The computer then creates... Supervised classification requires the analyst to select training areas where he knows what is on the ground and then digitize a polygon within that area… Mean Spectral Signatures Known Conifer Area Known Water Area Known Deciduous Area Digital Image Conifer Deciduous Water
- 31. Supervised Classification Multispectral Image Information (Classified Image) Mean Spectral Signatures Spectral Signature of Next Pixel to be Classified Conifer Deciduous Water Unknown
- 32. The Result: Image Signatures Water Conifer Deciduous Legend: Land Cover Map
- 34. Unsupervised Classification Digital Image The analyst requests the computer to examine the image and extract a number of spectrally distinct clusters… Spectrally Distinct Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4
- 35. Unsupervised Classification Output Classified Image Saved Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4 Unknown Next Pixel to be Classified
- 36. Unsupervised Classification It is a simple process to regroup (recode) the clusters into meaningful information classes (the legend). The result is essentially the same as that of the supervised classification: Conif. Hardw. Water Land Cover Map Legend Water Water Conifer Conifer Hardwood Hardwood Labels
- 37. MODEL FORMALISM
- 40. Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 It appears that the candidate pixel is closest to Signature 1. However, when we consider the variance around the signatures… Relative Reflectance Weighted Pixel Statistics Method
- 41. Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 The candidate pixel clearly belongs to the signature 2 group. Relative Reflectance Weighted Pixel Statistics Method
- 42. Weighted Pixel Statistics Method
- 43. Weighted Pixel Statistics Method
- 46. Results: 1 st Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 47. Quantitative Comparison 1 st Synthesized Scene
- 48. Results: 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 49. Qualitative Comparison 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 50. Quantitative Comparison 2 nd Synthesized Scene
- 51. Results: 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 52. Qualitative Comparison 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification
- 53. Quantitative Comparison 3 rd Synthesized Scene
- 57. Archaeological Site "Guachimontones", Jalisco Mexico
- 58. Simulation Results Scene from "Guachimontones" Original Scene WPS Classification
- 60. Simulation Results Scene from "La Vega" dam, Jalisco Mexico Original Scene WPS Classification
Editor's Notes
- Information usually gathered from spacecraft or an airplane, but can be a handheld or boom-mounted device. Originally defined in 1960’s according to Jensen, to encompass photogrammertry and information gathered from nonphotometric sources.