Image Classification Techniques in GIS
•Download as DOCX, PDF•
3 likes•3,026 views
This document discusses two main techniques for digital image classification in remote sensing: unsupervised and supervised classification. Unsupervised classification groups pixels into clusters based on reflectance properties and the user assigns land cover classes. Supervised classification involves selecting training sites for each land cover class, which are used to classify the image based on spectral signatures. Both techniques aim to categorize land cover features from remote sensing imagery.
Report
Share
Report
Share
Recommended
Image classification, remote sensing, P K MANI
Image classification involves using spectral bands of images to separate landscape features into categories. Pixels with similar spectral signatures are clustered and classified using techniques like maximum likelihood classification. This results in a classified image map where each pixel is assigned a land cover class. However, classified maps have errors, so accuracy assessment is important to estimate the map's accuracy. Supervised classification involves using training areas of known land cover to develop spectral signatures for classification, while unsupervised classification clusters pixels without prior class definitions.
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.
IMAGE INTERPRETATION TECHNIQUES of survey
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
Digital image processing
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Digital image processing
Digital image processing involves computer-based manipulation and interpretation of digital images. There are five broad types of operations: 1) image rectification and restoration to correct distorted or degraded images, 2) image enhancement to increase visual distinctions between features, 3) image classification to determine land cover by categorizing pixels, 4) data merging and GIS integration to combine image data with other geographic datasets, and 5) hyperspectral image analysis. Image rectification aims to correct geometric distortions from the image acquisition process.
Digital photogrammetry
This document discusses digital photogrammetry. It begins by explaining that digital photogrammetry uses digital images that are stored and processed on a computer, rather than hard copy photos. These digital images can come from satellites, airplanes, or cameras. The document then discusses some applications of digital photogrammetry like topographic mapping and creating orthophotos, digital elevation models (DEMs), and virtual landscapes. It also notes that nadir imagery and image overlap are needed to provide 3D information. Finally, the document lists some common products of digital photogrammetry such as maps, DEMs, and virtual landscapes.
Remote Sensing:. Image Filtering
The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image 'enhancement' is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement.
Filtering is used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. ‘Rough’ textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while ‘smooth’ areas with little variation have low spatial frequencies. A common filtering procedure involves moving a ‘matrix' of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value.
A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like 'low-pass filtering', the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. Directional, or edge detection filters are designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Recommended
Image classification, remote sensing, P K MANI
Image classification involves using spectral bands of images to separate landscape features into categories. Pixels with similar spectral signatures are clustered and classified using techniques like maximum likelihood classification. This results in a classified image map where each pixel is assigned a land cover class. However, classified maps have errors, so accuracy assessment is important to estimate the map's accuracy. Supervised classification involves using training areas of known land cover to develop spectral signatures for classification, while unsupervised classification clusters pixels without prior class definitions.
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.
IMAGE INTERPRETATION TECHNIQUES of survey
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
Digital image processing
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
Digital image processing
Digital image processing involves computer-based manipulation and interpretation of digital images. There are five broad types of operations: 1) image rectification and restoration to correct distorted or degraded images, 2) image enhancement to increase visual distinctions between features, 3) image classification to determine land cover by categorizing pixels, 4) data merging and GIS integration to combine image data with other geographic datasets, and 5) hyperspectral image analysis. Image rectification aims to correct geometric distortions from the image acquisition process.
Digital photogrammetry
This document discusses digital photogrammetry. It begins by explaining that digital photogrammetry uses digital images that are stored and processed on a computer, rather than hard copy photos. These digital images can come from satellites, airplanes, or cameras. The document then discusses some applications of digital photogrammetry like topographic mapping and creating orthophotos, digital elevation models (DEMs), and virtual landscapes. It also notes that nadir imagery and image overlap are needed to provide 3D information. Finally, the document lists some common products of digital photogrammetry such as maps, DEMs, and virtual landscapes.
Remote Sensing:. Image Filtering
The advantage of digital imagery is that it allows us to manipulate the digital pixel values in the image. Even after the radiometric corrections image may still not be optimized for visual interpretation. An image 'enhancement' is basically anything that makes it easier or better to visually interpret. An enhancement is performed for a specific application as well. This enhancement may be inappropriate for another purpose, which would demand a different type of enhancement.
Filtering is used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. ‘Rough’ textured areas of an image, where the changes in tone are abrupt, have high spatial frequencies, while ‘smooth’ areas with little variation have low spatial frequencies. A common filtering procedure involves moving a ‘matrix' of a few pixels in dimension (ie. 3x3, 5x5, etc.) over each pixel in the image, using mathematical calculation and replacing the central pixel with the new value.
A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. In some cases, like 'low-pass filtering', the enhanced image can actually look worse than the original, but such an enhancement was likely performed to help the interpreter see low spatial frequency features among the usual high frequency clutter found in an image. High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. Directional, or edge detection filters are designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
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.
Spatial interpolation techniques
Spatial interpolation techniques are used to estimate values at unsampled locations based on known sample points. There are two main types of interpolation methods: global methods which apply a single mathematical function to all points, and local methods which apply functions to subsets of points. Specific interpolation methods covered include Thiessen polygons, triangulated irregular networks, spatial moving average, trend surfaces, inverse distance weighting, and Kriging. Kriging is similar to inverse distance weighting but uses minimum variance to calculate weights.
Digital image classification
Digital image classification involves:
1) Sorting pixels into classes based on their spectral values using algorithms like supervised maximum likelihood classification or unsupervised isodata clustering.
2) Analyzing spectral patterns by examining pixels in feature space rather than image space. Distances between pixel vectors in feature spaces define class boundaries.
3) Validating classification results to determine accuracy by comparing to reference data. Problems can occur and techniques continue improving.
Raster data analysis
Raster data is represented by a grid of cells, where each cell contains numeric or qualitative values. Raster data comes from sources like images, maps, and satellite imagery. Common analyses of raster data include buffering, reclassification, hillshades, interpolation, and surface calculation. Buffering assigns "in" and "out" values to cells based on their distance from a feature. Reclassification reassigns cell values. Hillshades create shaded relief maps from elevation data. Interpolation estimates values between known data points. Surface calculation performs cell-by-cell mathematical functions on rasters.
Optical remote sensing
This document discusses different types of remote sensing systems used in civil engineering, including optical, photogrammetric, thermal, multispectral, hyperspectral, and panchromatic systems. It provides examples and specifications of various sensors, such as MODIS, AVIRIS, IKONOS, and WorldView. The document also covers digital image formats, photogrammetry, image distortions and displacements, reference ellipsoids, relief displacement, and methods of measuring heights from aerial photographs.
Remote Sensing: Image Classification
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric).
Unsupervised classification is where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.).
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Raster data and Vector data
This presentation is about the raster and vector data in GIS which is important and costly as well, through the presentation we will learn about both type of data.
Aerial photography.pptx
Aerial photography involves taking photographs from aircraft and is used for mapping and studying the Earth's surface. It has various uses like making pictorial representations, preparing base maps, photo interpretation, and expediting natural resource surveys. Factors like atmospheric conditions, aircraft, camera, and film processing influence aerial photographs. There are different types of aerial photographs based on the camera axis position and various stages involved in planning and executing aerial photography flights.
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.
Band ratioing presentation
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
Principle of aerial photography and types.ppt
Principle of aerial photography and photogrammetry, types of aerial photographs, scale of aerial photograph.
Remote sensing principles-spectral signature-spectural range
This document discusses the application of remote sensing and geographical information systems in civil engineering. It begins by defining remote sensing as the acquisition of information about an object without physical contact, typically by measuring electromagnetic radiation. It then defines geographical information systems as a system for capturing, storing, analyzing and presenting spatially referenced data. The document provides examples of how remote sensing data from sources like Google Earth can be spatially analyzed using a GIS. It proceeds to discuss key concepts in remote sensing including the electromagnetic spectrum, atmospheric interactions with radiation, and radiation measurement principles.
Stereoscopic parallax
This document discusses stereoscopy and parallax measurement in aerial photography. Stereoscopy uses two photographs of the same ground area taken from separate positions to create a stereo pair that enables three-dimensional viewing. Parallax is the displacement of an object caused by a change in the point of observation. Stereoscopic parallax occurs when photographs are taken of the same object from different positions, allowing measurement of differences in elevation.
Remote Sensing: Change Detection
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Aerial photogrammetry 05
This document provides an overview of aerial photogrammetry techniques including mosaics, mirror stereoscopes, and parallax bars. It defines mosaics as assemblies of overlapping aerial photographs. Mosaics are classified as controlled, uncontrolled, semi-controlled, strip, wet, or index. Mirror stereoscopes are used to view stereo pairs of photographs and measure parallax. Parallax bars allow measurement of parallax differences between points to determine flying height and elevation differences.
Image enhancement technique digital image analysis, in remote sensing ,P K MANI
Image enhancement techniques are used to improve the appearance of digital imagery for visual interpretation. There are linear and non-linear enhancement methods. Linear contrast stretching uniformly expands a narrow range of pixel values in an image histogram to utilize the full tonal range. Histogram equalization assigns a wider range of values to frequently occurring portions of the histogram. Spatial filtering can highlight or suppress specific spatial frequencies to enhance or reduce image texture. Principal components analysis transforms image bands into uncorrelated components that capture decreasing amounts of variance, reducing dimensionality while retaining most information. Vegetation indices like NDVI use ratios of infrared and red bands to identify vegetated areas.
Image classification arc gis10
This document provides an overview of image classification tools in ArcGIS 10:
- Image classification allows converting multiband satellite images into single band images with defined or undefined classes.
- ArcGIS 10 introduces an image classification toolbar with different classification techniques like unsupervised iso clustered classification.
- Training samples developed in other software can be used, and samples can be created or opened in the sample manager for supervised classification.
Remote sensing
This document provides an overview of remote sensing and image interpretation. It discusses key topics such as the use of maps as models to represent features on Earth, different types of map scales and spatial referencing systems, and how computers are used in map production. It also outlines the process of image interpretation, including levels of interpretation keys and basic elements to examine like size, shape, shadow, tone, color, and texture. Software programs used in map production like ArcGIS and types of data products from remote sensing are also reviewed.
Microwave remote sensing
Microwave remote sensing uses both passive and active sensors operating within the wavelength range of 1mm to 1m. Passive sensors such as microwave radiometers record naturally emitted energy, while active sensors like synthetic aperture radar (SAR) generate their own electromagnetic signals. SAR is an example of side-looking radar that uses signal processing to synthesize a very long antenna and improve azimuth resolution. Radar imagery exhibits characteristics like penetration of vegetation and clouds, day/night imaging, and sensitivity to surface properties. However, it also shows distortions from terrain relief and speckle noise from signal interference.
Geometry and types of aerial photographs
The document summarizes key properties and types of aerial photographs. It discusses that aerial photographs have angles and scales as important geometric properties. It describes the different types of photographs based on the camera axis direction (vertical, oblique, etc.), angle of coverage, film used (black and white, infrared, color), and scale determined by focal length and height. It provides details on vertical, low and high oblique, convergent, and trimetrogon photographs.
Thermal Remote Sensing
This document provides an overview of thermal remote sensing. It begins with an introduction to remote sensing and defines thermal remote sensing as measuring electromagnetic radiation in the thermal infrared region. It describes the atmospheric windows and fundamental radiation laws governing thermal remote sensing. Applications discussed include surface temperature detection, fire detection, and volcano monitoring. The document concludes with the advantages of being able to detect true temperatures and limitations such as difficulty maintaining sensor temperatures.
GIS and spatial-modelling
Geographic Information Systems (GIS) allow users to store, visualize, and analyze spatial data in digital formats. GIS can be used to ask questions about the location of things, what happens at locations, and how things are spatially related. Spatial data can be represented using vector models of discrete data like points, lines, and polygons, or raster models of continuous data like elevation or land cover which have no clear edges. Real world entities may have both discrete and continuous qualities, and different GIS data structures are suited to representing each type of data.
More Related Content
What's hot
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.
Spatial interpolation techniques
Spatial interpolation techniques are used to estimate values at unsampled locations based on known sample points. There are two main types of interpolation methods: global methods which apply a single mathematical function to all points, and local methods which apply functions to subsets of points. Specific interpolation methods covered include Thiessen polygons, triangulated irregular networks, spatial moving average, trend surfaces, inverse distance weighting, and Kriging. Kriging is similar to inverse distance weighting but uses minimum variance to calculate weights.
Digital image classification
Digital image classification involves:
1) Sorting pixels into classes based on their spectral values using algorithms like supervised maximum likelihood classification or unsupervised isodata clustering.
2) Analyzing spectral patterns by examining pixels in feature space rather than image space. Distances between pixel vectors in feature spaces define class boundaries.
3) Validating classification results to determine accuracy by comparing to reference data. Problems can occur and techniques continue improving.
Raster data analysis
Raster data is represented by a grid of cells, where each cell contains numeric or qualitative values. Raster data comes from sources like images, maps, and satellite imagery. Common analyses of raster data include buffering, reclassification, hillshades, interpolation, and surface calculation. Buffering assigns "in" and "out" values to cells based on their distance from a feature. Reclassification reassigns cell values. Hillshades create shaded relief maps from elevation data. Interpolation estimates values between known data points. Surface calculation performs cell-by-cell mathematical functions on rasters.
Optical remote sensing
This document discusses different types of remote sensing systems used in civil engineering, including optical, photogrammetric, thermal, multispectral, hyperspectral, and panchromatic systems. It provides examples and specifications of various sensors, such as MODIS, AVIRIS, IKONOS, and WorldView. The document also covers digital image formats, photogrammetry, image distortions and displacements, reference ellipsoids, relief displacement, and methods of measuring heights from aerial photographs.
Remote Sensing: Image Classification
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric).
Unsupervised classification is where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.).
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Raster data and Vector data
This presentation is about the raster and vector data in GIS which is important and costly as well, through the presentation we will learn about both type of data.
Aerial photography.pptx
Aerial photography involves taking photographs from aircraft and is used for mapping and studying the Earth's surface. It has various uses like making pictorial representations, preparing base maps, photo interpretation, and expediting natural resource surveys. Factors like atmospheric conditions, aircraft, camera, and film processing influence aerial photographs. There are different types of aerial photographs based on the camera axis position and various stages involved in planning and executing aerial photography flights.
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.
Band ratioing presentation
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
Principle of aerial photography and types.ppt
Principle of aerial photography and photogrammetry, types of aerial photographs, scale of aerial photograph.
Remote sensing principles-spectral signature-spectural range
This document discusses the application of remote sensing and geographical information systems in civil engineering. It begins by defining remote sensing as the acquisition of information about an object without physical contact, typically by measuring electromagnetic radiation. It then defines geographical information systems as a system for capturing, storing, analyzing and presenting spatially referenced data. The document provides examples of how remote sensing data from sources like Google Earth can be spatially analyzed using a GIS. It proceeds to discuss key concepts in remote sensing including the electromagnetic spectrum, atmospheric interactions with radiation, and radiation measurement principles.
Stereoscopic parallax
This document discusses stereoscopy and parallax measurement in aerial photography. Stereoscopy uses two photographs of the same ground area taken from separate positions to create a stereo pair that enables three-dimensional viewing. Parallax is the displacement of an object caused by a change in the point of observation. Stereoscopic parallax occurs when photographs are taken of the same object from different positions, allowing measurement of differences in elevation.
Remote Sensing: Change Detection
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Aerial photogrammetry 05
This document provides an overview of aerial photogrammetry techniques including mosaics, mirror stereoscopes, and parallax bars. It defines mosaics as assemblies of overlapping aerial photographs. Mosaics are classified as controlled, uncontrolled, semi-controlled, strip, wet, or index. Mirror stereoscopes are used to view stereo pairs of photographs and measure parallax. Parallax bars allow measurement of parallax differences between points to determine flying height and elevation differences.
Image enhancement technique digital image analysis, in remote sensing ,P K MANI
Image enhancement techniques are used to improve the appearance of digital imagery for visual interpretation. There are linear and non-linear enhancement methods. Linear contrast stretching uniformly expands a narrow range of pixel values in an image histogram to utilize the full tonal range. Histogram equalization assigns a wider range of values to frequently occurring portions of the histogram. Spatial filtering can highlight or suppress specific spatial frequencies to enhance or reduce image texture. Principal components analysis transforms image bands into uncorrelated components that capture decreasing amounts of variance, reducing dimensionality while retaining most information. Vegetation indices like NDVI use ratios of infrared and red bands to identify vegetated areas.
Image classification arc gis10
This document provides an overview of image classification tools in ArcGIS 10:
- Image classification allows converting multiband satellite images into single band images with defined or undefined classes.
- ArcGIS 10 introduces an image classification toolbar with different classification techniques like unsupervised iso clustered classification.
- Training samples developed in other software can be used, and samples can be created or opened in the sample manager for supervised classification.
Remote sensing
This document provides an overview of remote sensing and image interpretation. It discusses key topics such as the use of maps as models to represent features on Earth, different types of map scales and spatial referencing systems, and how computers are used in map production. It also outlines the process of image interpretation, including levels of interpretation keys and basic elements to examine like size, shape, shadow, tone, color, and texture. Software programs used in map production like ArcGIS and types of data products from remote sensing are also reviewed.
Microwave remote sensing
Microwave remote sensing uses both passive and active sensors operating within the wavelength range of 1mm to 1m. Passive sensors such as microwave radiometers record naturally emitted energy, while active sensors like synthetic aperture radar (SAR) generate their own electromagnetic signals. SAR is an example of side-looking radar that uses signal processing to synthesize a very long antenna and improve azimuth resolution. Radar imagery exhibits characteristics like penetration of vegetation and clouds, day/night imaging, and sensitivity to surface properties. However, it also shows distortions from terrain relief and speckle noise from signal interference.
Geometry and types of aerial photographs
The document summarizes key properties and types of aerial photographs. It discusses that aerial photographs have angles and scales as important geometric properties. It describes the different types of photographs based on the camera axis direction (vertical, oblique, etc.), angle of coverage, film used (black and white, infrared, color), and scale determined by focal length and height. It provides details on vertical, low and high oblique, convergent, and trimetrogon photographs.
What's hot (20)
Remote sensing principles-spectral signature-spectural range
Remote sensing principles-spectral signature-spectural range
Image enhancement technique digital image analysis, in remote sensing ,P K MANI
Image enhancement technique digital image analysis, in remote sensing ,P K MANI
Viewers also liked
Thermal Remote Sensing
This document provides an overview of thermal remote sensing. It begins with an introduction to remote sensing and defines thermal remote sensing as measuring electromagnetic radiation in the thermal infrared region. It describes the atmospheric windows and fundamental radiation laws governing thermal remote sensing. Applications discussed include surface temperature detection, fire detection, and volcano monitoring. The document concludes with the advantages of being able to detect true temperatures and limitations such as difficulty maintaining sensor temperatures.
GIS and spatial-modelling
Geographic Information Systems (GIS) allow users to store, visualize, and analyze spatial data in digital formats. GIS can be used to ask questions about the location of things, what happens at locations, and how things are spatially related. Spatial data can be represented using vector models of discrete data like points, lines, and polygons, or raster models of continuous data like elevation or land cover which have no clear edges. Real world entities may have both discrete and continuous qualities, and different GIS data structures are suited to representing each type of data.
Exploring spatial data in GIS Environment
This document outlines the agenda for day 5 of a GIS training at IOE Pulchowk, including understanding file types like shapefiles and their components, exploring data properties and tools in the Arc Toolbox, and three assignments - creating a proportional point symbol map of gas stations, making a custom toolbar, and finding differences between two point layers.
Thermal remote sensing
The document provides an overview of thermal remote sensing. It discusses key concepts like the thermal infrared spectrum, atmospheric windows and absorption bands, fundamental radiation laws, thermal data acquisition using sensors, and applications in mapping forest fires, urban heat islands, volcanoes, and military purposes. Thermal remote sensing allows measuring the true temperature of objects and detecting features not visible in optical remote sensing. It has advantages like temperature measurement but maintaining sensors at low temperatures can be challenging.
gis spatial data and maps
This document discusses key concepts related to digital maps and cartography. It defines important spatial data components like entities, attributes, and relationships. It also outlines advantages of digital maps over paper maps and describes key cartographic considerations like map scale, data classification, and generalization. Finally, it discusses different types of map projections and coordinate systems used to represent the spherical Earth on a flat surface.
Introduction to Geographical Information System, GIS data models, spatial dat...
Geospatial data is data about the geographic location of earth surface features and boundaries on Earth. Nowadays spatial data is used in every field of society and due to the advancements in spatial data acquisition technologies, such as the advancements in the satellite sensor technologies, high precision digital cameras used in the capturing of photogram metric images and high precision land surveys are producing mass high precision spatial data. Due to these issues nowadays sensitivity of spatial data has increased too many folds. To store such high precision data onto the database is a big challenge today. Major security concerns of the geospatial data are based on authorization, authentication, access control, integrity, security and secure transmission of spatial data over the network and transmission media. In this paper the major security concerns of geospatial data, various data models, introduction to Geographical Information System, GIS data models, spatial data, and spatial database. The basic objective is to developed secure access control mechanism.
Viewers also liked (6)
Introduction to Geographical Information System, GIS data models, spatial dat...
Introduction to Geographical Information System, GIS data models, spatial dat...
Similar to Image Classification Techniques in GIS
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 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.
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.
Lrm 111 intro remote sensing lesson vii
This document discusses image interpretation and classification techniques in remote sensing. It describes visual interpretation which takes advantage of human skills to recognize information in images based on characteristics like size, color, texture and shape. Digital image classification uses computer algorithms to sort pixels into classes based on brightness values. There are supervised and unsupervised methods. Supervised classification involves training the computer to recognize patterns in data and then categorizing pixels based on spectral signatures developed during training. Unsupervised classification automatically identifies distinct spectral classes present in an image without training data.
Image classification & retrieval
Digital image classification involves categorizing each pixel in an image based on its spectral information and assigning it to a class or theme like water, forest, or crop. This results in a thematic map of the original image. There are two main classification methods: supervised classification where the analyst identifies training samples to teach the computer how to classify, and unsupervised classification where computer clustering algorithms group pixels without training to determine natural spectral classes.
DIGITAL IMAGE ANALYSIS
This document discusses image classification and analysis techniques. It describes supervised classification where an analyst identifies training areas to classify pixels based on spectral information. Unsupervised classification uses clustering algorithms to automatically group spectrally similar pixels without training areas. Common classification procedures include supervised classification using training areas, unsupervised classification using clustering, and hybrid methods. Maximum likelihood and minimum distance are described as supervised classification algorithms.
Remote Sensing Image Scene Classification
Project to show satellite imagery prediction based on Deep Learning methods and prove that CNN+ BoVW <= BoCF.
image_classification.pptx
Image classification as a process of assigning all pixels in the image to particular classes or themes based on spectral information represented by the digital numbers (DNs). The classified image comprises a mosaic of pixels, each of which belong to a particular theme and is a thematic map of the original image.
Approaches to Classification There are two general approaches to image classification:
Supervised Classification: It is the process of identification of classes within a remote sensing data with inputs from and as directed by the user in the form of training data, and
Unsupervised Classification: It is the process of automatic identification of natural groups or structures within a remote sensing data.
Blind Source Camera Identification
This is a research area of digital forensic . how to identify which picture is taken by which camera based on its feature not watermark
Image classification
This document discusses supervised image classification using support vector machines (SVM). It begins with an abstract and introduction on image classification and supervised classification. It then discusses the methodology, including pre-processing, segmentation, feature extraction, and classification steps. SVM classification involves extracting pixels from images into a data frame, using random pixels for training and the rest for testing. The SVM classifier is created using the training set and histograms. Accuracy is measured using precision formulas. Implementing the process with horse images achieved up to 90% accuracy when retraining with additional images.
final_year_project
This document summarizes a project that developed an automatic classification model for remote sensing images using texture features and classifiers. It presents techniques used like Gabor and Gaussian filters for feature extraction. A system was designed and implemented in MATLAB for image processing and RapidMiner for classification using KNN, SVM and neural networks. The models were applied to classify land cover types in images and evaluate changes over time. Evaluation showed SVM achieved the best accuracy of 97.94% while requiring less time than the neural network. Applications of the automatic classification model include soil assessment, land use mapping and monitoring of environmental changes.
D43031521
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.
Features Analysis in CBIR Systems
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
Deep Computer Vision - 1.pptx
Deep computer vision uses deep learning and machine learning techniques to build powerful vision systems that can analyze raw visual inputs and understand what objects are present and where they are located. Convolutional neural networks (CNNs) are well-suited for computer vision tasks as they can learn visual features and hierarchies directly from data through operations like convolution, non-linearity, and pooling. CNNs apply filters to extract features, introduce non-linearity, and use pooling to reduce dimensionality while preserving spatial data. This repeating structure allows CNNs to learn increasingly complex features to perform tasks like image classification, object detection, semantic segmentation, and continuous control from raw pixels.
R sprojec3
The document compares image classification in ENVI and eCognition software. It details the image classification process for each: in ENVI, images are corrected, filtered, and then classified as supervised or unsupervised; in eCognition, images undergo multiresolution segmentation into objects before creating classes and performing nearest neighbor classification. The conclusion states that ENVI classification is more standardized and supervised, while eCognition uses an object-oriented, hierarchical, trial-and-error approach to segmentation and classification.
An Automatic Color Feature Vector Classification Based on Clustering Method
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
Analysis of Classification Approaches
Abstract: Object Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM) and Fuzzy Classification.Keywords: Image Classification, Artificial Neural Network, Decision Tree, Support Vector Machine, Fuzzy Classifier.
Title: Analysis of Classification Approaches
Author: Robin Kumar
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Framework on Retrieval of Hypermedia Data using Data mining Technique
Image Annotation is a method to reveal the meaning for a specific image .The embedded meaning in the image is identified and mined. The Scenario is identified through the image annotation scheme with in a provided training. The focus is on the blur images, noisy images and images with pixels lost. The image annotation can be done on the good resolution image. The analysis carried outon the image data to derive the information and image restoration takes place. Image mining deals with extracting embedded details, patterns and their relationship in images. Embedded details in the image could be extracted using high-level features that are robust. Inpainting techniques can be utilized for cleaning the image .The analytics is applied on enormous amount of data, techniques performed on the test images sets for better accuracy.
07 Image classification.pptx
Image classification involves sorting pixels into categories based on their spectral signatures. There are two main approaches: unsupervised classification automatically groups similar pixels into spectral classes which the analyst then labels, while supervised classification uses training sites of known land cover to classify pixels based on their likelihood of belonging to a class. The document provides details on processes, techniques, advantages and disadvantages of each approach.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...
This document summarizes various content-based image retrieval techniques using clustering methods for large datasets. It discusses clustering algorithms like K-means, hierarchical clustering, graph-based clustering and a proposed hybrid divide-and-conquer K-means method. The hybrid method uses hierarchical and divide-and-conquer approaches to improve K-means performance for high dimensional datasets. Content-based image retrieval relies on automatically extracted visual features like color, texture and shape for image classification and retrieval.
Similar to Image Classification Techniques in GIS (20)
An Automatic Color Feature Vector Classification Based on Clustering Method
An Automatic Color Feature Vector Classification Based on Clustering Method
Framework on Retrieval of Hypermedia Data using Data mining Technique
Framework on Retrieval of Hypermedia Data using Data mining Technique
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...
More from Sadia Rahat
Climate Change
Climate Change:
Definitions: USA EPA!
Climate change refers to any significant change in the measures of climate lasting for an extended period of time. In other words, climate change includes major changes in temperature, precipitation, or wind patterns, among other effects, that occur over several decades or longer.
History:
20 hottest years in the last and present century occurred after 1980.
KYOTO PROTOCOL
On February 2005 KYOTO PROTOCOL came into force in most of the 169 countries of the world.
It was decided that by 2010 GHG emissions would be reduced by 5.2% below 1990 level.
Causes:
Constantly emitting GHGs are the prime cause of it.
1.Water vapor (H. 2O)
2.Carbon dioxide (CO2)
3.Methane (CH4)
4.Nitrous oxide (N2O)
5.Ozone (O3)
6.Chloro-fluorocarbons (CFCs)
Effects:
Ill health of mother earth.
Future:
A darker future awaits for us if we don't take rationale steps right now.
EFFECTUAL IMPLICATION OF TOTAL QUALITY MANAGEMENT WITHIN AMAZON.COM INC.
This document discusses the fundamentals and application of total quality management (TQM) within Amazon.com Inc. It outlines key TQM fundamentals like demand forecasting, inventory management, transportation, customer service, distribution, procurement, strategic planning, and risk management. It then provides details on how Amazon applies TQM principles within its supply chain, focusing on value proposition, drop shipment model, and strategic supply chain management. Overall, the document analyzes how TQM has contributed to Amazon's success.
Spectral Signature
Spectral signatures are the specific combination of electromagnetic radiation emitted or reflected at different wavelengths that can uniquely identify an object. A spectral signature depends on factors like the solar angle, view angle, surface wetness, and surrounding materials. While spectral signatures can indicate the composition of stars or distinguish between minerals based on their chemical makeup, they have limitations and may not always provide enough information, such as indicating the desired parameter for vegetation or simulating what a satellite sensor would see due to differences between laboratory and field measurements.
Resampling- GIS
Resampling is defined as deriving pixel values for a new image from an existing image. There are two main types of resampling: nearest neighbor and bilinear interpolation. Nearest neighbor assigns the value of the nearest pixel in the input image to pixels in the output image, producing blocky results best used for discrete data. Bilinear interpolation takes the weighted average of the nearest four pixels, producing smoother results best used for continuous data. Cubic convolution is similar but uses sixteen nearest pixels, providing the sharpest results but longest processing time and is best for continuous data.
Heads up digitization- ON THE FLY- GIS
This document discusses heads-up digitization, which involves manually tracing features on a computer screen using raster data as a backdrop. It describes different types of digitization including heads-down and heads-up methods. Heads-up digitization allows an operator to use a scanned image on screen to trace and digitize geographical features into GIS layers. The document also discusses on-the-fly projection in GIS software, which automatically projects additional data layers to match the coordinate system of the base map. On-the-fly generalization is useful for applications requiring real-time map display at multiple scales, such as web mapping, mobile mapping, and decision support systems.
Atmospheric windows
Certain wavelengths of light can penetrate the atmosphere better than others. These transparent regions are called "atmospheric windows". The figure shows that visible light and radio frequencies experience the least absorption as they travel through the atmosphere. These are the dominant windows. X-rays and UV light face strong absorption, while gamma rays and infrared see somewhat less absorption. Atmospheric windows may refer specifically to the radio, infrared, and visible portions of the electromagnetic spectrum.
ESTIMATION OF THE CONCENTRATION OF POTASSIUM IN BANANA SAMPLE BY USING ATOMIC...
ESTIMATION OF THE CONCENTRATION OF POTASSIUM IN BANANA SAMPLE BY USING ATOMIC ABSORPTION SPECTROPHOTOMETER
ESTIMATION OF POTASSIUM IN BANANA SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER ...
ESTIMATION OF POTASSIUM IN BANANA SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER ...
The document describes a procedure to estimate the concentration of sodium in a tap water sample using flame emission spectrophotometry. The procedure involves preparing standard sodium chloride solutions of known concentrations, measuring their emissions using the spectrophotometer, and generating a calibration curve. The tap water sample's emission is also measured and its sodium concentration is determined using the calibration curve. The estimated sodium concentration in the tap water sample was 1.45 ppm.
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY ATOMIC ABSORPTION SPECTROPHOTOMETER
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY ATOMIC ABSORPTION SPECTROPHOTOMETER
ESTIMATION OF THE CONCENTRATION OF PHOSPHATE IN LAUNDRY EFFLUENT SAMPLE BY US...
ESTIMATION OF THE CONCENTRATION OF PHOSPHATE IN LAUNDRY EFFLUENT SAMPLE BY USING VISIBLE SPECTROPHOTOMETER
ESTIMATION OF DO, BOD AND COD IN CANAL WATER SAMPLE
The document discusses the estimation of dissolved oxygen (DO), biological oxygen demand (BOD), and chemical oxygen demand (COD) in a canal water sample. DO was found to be 3.20 ppm, BOD was 54.24 ppm, and COD was 220 ppm in the sample. BOD measures the amount of oxygen consumed by microorganisms to break down organic matter over 5 days. COD uses a strong chemical oxidant to measure total organic compounds and some inorganic compounds. While related, BOD and COD measure oxygen demand slightly differently. BOD is more relevant for organic-rich waters, while COD provides a faster test that is not affected by toxins.
Estimation of Calcium in Fish Pond water by Complexometric Method
This document describes a method to estimate the calcium content in fish pond water using complexometric titration. Key points:
- Calcium ions in the water sample are complexed with the indicator murexide, producing a reddish color.
- The sample is then titrated with EDTA. EDTA forms a stronger complex with calcium ions, displacing the calcium-murexide complex.
- The endpoint is detected when the solution color changes from reddish to violet, indicating all calcium ions have been complexed by EDTA.
- Calculations using the titration data allow determining the concentration of calcium ions in the original water sample in units of ppm. The results found
Estimation of Chlorides in any water body ( Pond water)
This document describes a gravimetric analysis to estimate the amount of chlorides in a pond water sample. A silver nitrate solution is added to the water sample to form an insoluble silver chloride precipitate. The precipitate is filtered, dried, and weighed. Calculations based on the mass of the precipitate indicate the pond water sample contains approximately 0.297% chlorides or 2,950 parts per million, which exceeds the WHO drinking water guideline of 250 ppm and other standards for chloride levels in water. Proper precautions are needed when handling silver nitrate.
ASSIGNMENT: WRITE DOWN THE CLASSICAL METHODS WHICH GIVE US QUALITATIVE AND QU...
The document summarizes classical qualitative and quantitative analytical techniques used in environmental analysis. Qualitative methods identify the presence of substances but not amounts, using tests like indicators, flame tests, and solubility. Quantitative methods determine exact amounts or concentrations, such as acid-base titration, gravimetric analysis weighing precipitates, and volumetric titration measuring reagent volumes.
FIELD TOUR REPORT TOUR TO ISLAMABAD, ABOTABAD, MUREE, MUZAFFARABAD, MANGLA, ...
FIELD TOUR REPORT
TOUR TO ISLAMABAD, ABOTABAD, MUREE, MUZAFFARABAD, MANGLA, AZAD LASHMIR, BALAKOT
Acid rain
Acid rain is caused by emissions of sulfur and nitrogen compounds from human sources like fossil fuel burning. It makes rainwater acidic and harms aquatic life, forests, and infrastructure. Areas with granite bedrock and those downwind of industrial regions are most affected. To reduce acid rain, emissions must be decreased through cleaner energy sources and regulated smokestack emissions. Affected ecosystems can also be treated by adding limestone to neutralize acidity.
E waste
This document defines e-waste as used or obsolete electronic devices such as TVs, computers, phones, and other household appliances. It discusses the history of e-waste and how informal processing of e-waste in developing countries can cause health and environmental issues due to toxic components like lead, cadmium, and brominated flame retardants. The document also outlines the types of e-waste, initiatives like StEP to address e-waste issues, techniques for e-waste management including recycling and consumer awareness, and both hazardous and non-hazardous substances commonly found in e-waste.
Biodiversity of reptiles in Pakistan
This document summarizes the biodiversity of reptiles found in Pakistan. It lists 195 reptile species known in Pakistan, including 13 endemic species. It provides tables of common and scientific names of reptiles classified by family. It notes two endemic crocodile species, the Mugger and Gharial, and lists internationally threatened reptile species. Snakes present include cobras, kraits, and vipers. Common lizards mentioned are the yellow monitor, spiny-tailed lizard, and leopard gecko.
AIR POLLUTION EPISODE DHAKA-AIR POLLUTION EPISODE (1997) ARSENIC POISONING IN...
The document discusses the 1997 air pollution episode in Dhaka, Bangladesh. It notes that Dhaka has extremely high levels of air pollutants that exceed international standards. Major sources of pollution include vehicles, brick kilns burning hazardous materials, and a growing population. High concentrations of particulate matter, sulfur dioxide, lead, and other pollutants have caused many health issues for residents such as respiratory diseases and cancer. Immediate action is needed to improve emissions controls, transportation systems, and increase green spaces to address Dhaka's severe air pollution problems.
More from Sadia Rahat (20)
EFFECTUAL IMPLICATION OF TOTAL QUALITY MANAGEMENT WITHIN AMAZON.COM INC.
EFFECTUAL IMPLICATION OF TOTAL QUALITY MANAGEMENT WITHIN AMAZON.COM INC.
ESTIMATION OF THE CONCENTRATION OF POTASSIUM IN BANANA SAMPLE BY USING ATOMIC...
ESTIMATION OF THE CONCENTRATION OF POTASSIUM IN BANANA SAMPLE BY USING ATOMIC...
ESTIMATION OF POTASSIUM IN BANANA SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER ...
ESTIMATION OF POTASSIUM IN BANANA SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER ...
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER ...
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY FLAME EMISSION SPECTROPHOTOMETER ...
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY ATOMIC ABSORPTION SPECTROPHOTOMETER
ESTIMATION OF SODIUM IN TAP WATER SAMPLE BY ATOMIC ABSORPTION SPECTROPHOTOMETER
ESTIMATION OF THE CONCENTRATION OF PHOSPHATE IN LAUNDRY EFFLUENT SAMPLE BY US...
ESTIMATION OF THE CONCENTRATION OF PHOSPHATE IN LAUNDRY EFFLUENT SAMPLE BY US...
ESTIMATION OF DO, BOD AND COD IN CANAL WATER SAMPLE
ESTIMATION OF DO, BOD AND COD IN CANAL WATER SAMPLE
Estimation of Calcium in Fish Pond water by Complexometric Method
Estimation of Calcium in Fish Pond water by Complexometric Method
Estimation of Chlorides in any water body ( Pond water)
Estimation of Chlorides in any water body ( Pond water)
ASSIGNMENT: WRITE DOWN THE CLASSICAL METHODS WHICH GIVE US QUALITATIVE AND QU...
ASSIGNMENT: WRITE DOWN THE CLASSICAL METHODS WHICH GIVE US QUALITATIVE AND QU...
FIELD TOUR REPORT TOUR TO ISLAMABAD, ABOTABAD, MUREE, MUZAFFARABAD, MANGLA, ...
FIELD TOUR REPORT TOUR TO ISLAMABAD, ABOTABAD, MUREE, MUZAFFARABAD, MANGLA, ...
AIR POLLUTION EPISODE DHAKA-AIR POLLUTION EPISODE (1997) ARSENIC POISONING IN...
AIR POLLUTION EPISODE DHAKA-AIR POLLUTION EPISODE (1997) ARSENIC POISONING IN...
Recently uploaded
Wildlife-AnIntroduction.pdf so that you know more about our environment
All about wildlife, if you are fond of animals and wildlife then do follow us for more content like this
在线办理(lboro毕业证书)拉夫堡大学毕业证学历证书一模一样
学校原件一模一样【微信：741003700 】《(lboro毕业证书)拉夫堡大学毕业证学历证书》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Improving the Management of Peatlands and the Capacities of Stakeholders in I...
Improving the Management of Peatlands and the Capacities of Stakeholders in I...Global Landscapes Forum (GLF)
Presented by Sonya Dewi at GLF Peatlands 2024 - 360 Degree Approach to Improving Capacity of Multiple Stakeholders to Manage Peatland SustainablyLessons from operationalizing integrated landscape approaches
Presented by James Reed at "9th Landscape Sustainability Science Forum" on 11 May 2024
Evolving Lifecycles with High Resolution Site Characterization (HRSC) and 3-D...
The incorporation of a 3DCSM and completion of HRSC provided a tool for enhanced, data-driven, decisions to support a change in remediation closure strategies. Currently, an approved pilot study has been obtained to shut-down the remediation systems (ISCO, P&T) and conduct a hydraulic study under non-pumping conditions. A separate micro-biological bench scale treatability study was competed that yielded positive results for an emerging innovative technology. As a result, a field pilot study has commenced with results expected in nine-twelve months. With the results of the hydraulic study, field pilot studies and an updated risk assessment leading site monitoring optimization cost lifecycle savings upwards of $15MM towards an alternatively evolved best available technology remediation closure strategy.
Microbial characterisation and identification, and potability of River Kuywa ...
Microbial characterisation and identification, and potability of River Kuywa ...Open Access Research Paper
Water contamination is one of the major causes of water borne diseases worldwide. In Kenya, approximately 43% of people lack access to potable water due to human contamination. River Kuywa water is currently experiencing contamination due to human activities. Its water is widely used for domestic, agricultural, industrial and recreational purposes. This study aimed at characterizing bacteria and fungi in river Kuywa water. Water samples were randomly collected from four sites of the river: site A (Matisi), site B (Ngwelo), site C (Nzoia water pump) and site D (Chalicha), during the dry season (January-March 2018) and wet season (April-July 2018) and were transported to Maseno University Microbiology and plant pathology laboratory for analysis. The characterization and identification of bacteria and fungi were carried out using standard microbiological techniques. Nine bacterial genera and three fungi were identified from Kuywa river water. Clostridium spp., Staphylococcus spp., Enterobacter spp., Streptococcus spp., E. coli, Klebsiella spp., Shigella spp., Proteus spp. and Salmonella spp. Fungi were Fusarium oxysporum, Aspergillus flavus complex and Penicillium species. Wet season recorded highest bacterial and fungal counts (6.61-7.66 and 3.83-6.75cfu/ml) respectively. The results indicated that the river Kuywa water is polluted and therefore unsafe for human consumption before treatment. It is therefore recommended that the communities to ensure that they boil water especially for drinking.
Optimizing Post Remediation Groundwater Performance with Enhanced Microbiolog...
Results of geophysics and pneumatic injection pilot tests during 2003 – 2007 yielded significant positive results for injection delivery design and contaminant mass treatment, resulting in permanent shut-down of an existing groundwater Pump & Treat system.
Accessible source areas were subsequently removed (2011) by soil excavation and treated with the placement of Emulsified Vegetable Oil EVO and zero-valent iron ZVI to accelerate treatment of impacted groundwater in overburden and weathered fractured bedrock. Post pilot test and post remediation groundwater monitoring has included analyses of CVOCs, organic fatty acids, dissolved gases and QuantArray® -Chlor to quantify key microorganisms (e.g., Dehalococcoides, Dehalobacter, etc.) and functional genes (e.g., vinyl chloride reductase, methane monooxygenase, etc.) to assess potential for reductive dechlorination and aerobic cometabolism of CVOCs.
In 2022, the first commercial application of MetaArray™ was performed at the site. MetaArray™ utilizes statistical analysis, such as principal component analysis and multivariate analysis to provide evidence that reductive dechlorination is active or even that it is slowing. This creates actionable data allowing users to save money by making important site management decisions earlier.
The results of the MetaArray™ analysis’ support vector machine (SVM) identified groundwater monitoring wells with a 80% confidence that were characterized as either Limited for Reductive Decholorination or had a High Reductive Reduction Dechlorination potential. The results of MetaArray™ will be used to further optimize the site’s post remediation monitoring program for monitored natural attenuation.
Peatlands of Latin America and the Caribbean
Presented by The Global Peatlands Assessment: Mapping, Policy, and Action at GLF Peatlands 2024 - The Global Peatlands Assessment: Mapping, Policy, and Action
Overview of the Global Peatlands Assessment
Presented by Patrick Scheel at GLF Peatlands 2024 - The Global Peatlands Assessment: Mapping, Policy, and Action
Peatland Management in Indonesia, Science to Policy and Knowledge Education
Peatland Management in Indonesia, Science to Policy and Knowledge EducationGlobal Landscapes Forum (GLF)
Presented by Feri Johana at GLF Peatlands 2024 - 360 Degree Approach to Improving Capacity of Multiple Stakeholders to Manage Peatland SustainablyEpcon is One of the World's leading Manufacturing Companies.
Epcon is One of the World's leading Manufacturing Companies. With over 4000 installations worldwide, EPCON has been pioneering new techniques since 1977 that have become industry standards now. Founded in 1977, Epcon has grown from a one-man operation to a global leader in developing and manufacturing innovative air pollution control technology and industrial heating equipment.
原版制作(Manitoba毕业证书)曼尼托巴大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(Manitoba毕业证书)曼尼托巴大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Enhanced action and stakeholder engagement for sustainable peatland management
Enhanced action and stakeholder engagement for sustainable peatland managementGlobal Landscapes Forum (GLF)
Presented by Jan Peters at GLF Peatlands 2024 - The Global Peatlands Assessment: Mapping, Policy, and ActionRoHS stands for Restriction of Hazardous Substances, which is also known as t...
RoHS stands for Restriction of Hazardous Substances, which is also known as the Directive 2002/95/EC. It includes the restrictions for the use of certain hazardous substances in electrical and electronic equipment. RoHS is a WEEE (Waste of Electrical and Electronic Equipment).
Global Peatlands Map and Hotspot Explanation Atlas
Presented by Alexandra Barthelmes at GLF Peatlands 2024 - The Global Peatlands Assessment: Mapping, Policy, and Action
Kinetic studies on malachite green dye adsorption from aqueous solutions by A...
Kinetic studies on malachite green dye adsorption from aqueous solutions by A...Open Access Research Paper
Water polluted by dyestuffs compounds is a global threat to health and the environment; accordingly, we prepared a green novel sorbent chemical and Physical system from an algae, chitosan and chitosan nanoparticle and impregnated with algae with chitosan nanocomposite for the sorption of Malachite green dye from water. The algae with chitosan nanocomposite by a simple method and used as a recyclable and effective adsorbent for the removal of malachite green dye from aqueous solutions. Algae, chitosan, chitosan nanoparticle and algae with chitosan nanocomposite were characterized using different physicochemical methods. The functional groups and chemical compounds found in algae, chitosan, chitosan algae, chitosan nanoparticle, and chitosan nanoparticle with algae were identified using FTIR, SEM, and TGADTA/DTG techniques. The optimal adsorption conditions, different dosages, pH and Temperature the amount of algae with chitosan nanocomposite were determined. At optimized conditions and the batch equilibrium studies more than 99% of the dye was removed. The adsorption process data matched well kinetics showed that the reaction order for dye varied with pseudo-first order and pseudo-second order. Furthermore, the maximum adsorption capacity of the algae with chitosan nanocomposite toward malachite green dye reached as high as 15.5mg/g, respectively. Finally, multiple times reusing of algae with chitosan nanocomposite and removing dye from a real wastewater has made it a promising and attractive option for further practical applications.
Improving the viability of probiotics by encapsulation methods for developmen...
Improving the viability of probiotics by encapsulation methods for developmen...Open Access Research Paper
The popularity of functional foods among scientists and common people has been increasing day by day. Awareness and modernization make the consumer think better regarding food and nutrition. Now a day’s individual knows very well about the relation between food consumption and disease prevalence. Humans have a diversity of microbes in the gut that together form the gut microflora. Probiotics are the health-promoting live microbial cells improve host health through gut and brain connection and fighting against harmful bacteria. Bifidobacterium and Lactobacillus are the two bacterial genera which are considered to be probiotic. These good bacteria are facing challenges of viability. There are so many factors such as sensitivity to heat, pH, acidity, osmotic effect, mechanical shear, chemical components, freezing and storage time as well which affects the viability of probiotics in the dairy food matrix as well as in the gut. Multiple efforts have been done in the past and ongoing in present for these beneficial microbial population stability until their destination in the gut. One of a useful technique known as microencapsulation makes the probiotic effective in the diversified conditions and maintain these microbe’s community to the optimum level for achieving targeted benefits. Dairy products are found to be an ideal vehicle for probiotic incorporation. It has been seen that the encapsulated microbial cells show higher viability than the free cells in different processing and storage conditions as well as against bile salts in the gut. They make the food functional when incorporated, without affecting the product sensory characteristics.
Promoting Multilateral Cooperation for Sustainable Peatland management
Presented by Agus Julianto on GLF Peatlands 2024 - 360 Degree Approach to Improving Capacity of Multiple Stakeholders to Manage Peatland Sustainably
原版制作(Newcastle毕业证书)纽卡斯尔大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(Newcastle毕业证书)纽卡斯尔大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Recently uploaded (20)
Wildlife-AnIntroduction.pdf so that you know more about our environment
Wildlife-AnIntroduction.pdf so that you know more about our environment
Improving the Management of Peatlands and the Capacities of Stakeholders in I...
Improving the Management of Peatlands and the Capacities of Stakeholders in I...
Lessons from operationalizing integrated landscape approaches
Lessons from operationalizing integrated landscape approaches
Evolving Lifecycles with High Resolution Site Characterization (HRSC) and 3-D...
Evolving Lifecycles with High Resolution Site Characterization (HRSC) and 3-D...
Microbial characterisation and identification, and potability of River Kuywa ...
Microbial characterisation and identification, and potability of River Kuywa ...
world-environment-day-2024-240601103559-14f4c0b4.pptx
world-environment-day-2024-240601103559-14f4c0b4.pptx
Optimizing Post Remediation Groundwater Performance with Enhanced Microbiolog...
Optimizing Post Remediation Groundwater Performance with Enhanced Microbiolog...
Peatland Management in Indonesia, Science to Policy and Knowledge Education
Peatland Management in Indonesia, Science to Policy and Knowledge Education
Epcon is One of the World's leading Manufacturing Companies.
Epcon is One of the World's leading Manufacturing Companies.
Enhanced action and stakeholder engagement for sustainable peatland management
Enhanced action and stakeholder engagement for sustainable peatland management
RoHS stands for Restriction of Hazardous Substances, which is also known as t...
RoHS stands for Restriction of Hazardous Substances, which is also known as t...
Global Peatlands Map and Hotspot Explanation Atlas
Global Peatlands Map and Hotspot Explanation Atlas
Kinetic studies on malachite green dye adsorption from aqueous solutions by A...
Kinetic studies on malachite green dye adsorption from aqueous solutions by A...
Improving the viability of probiotics by encapsulation methods for developmen...
Improving the viability of probiotics by encapsulation methods for developmen...
Promoting Multilateral Cooperation for Sustainable Peatland management
Promoting Multilateral Cooperation for Sustainable Peatland management
Image Classification Techniques in GIS
- 1. DIGITAL IMAGE CLASSIFICATION TECHNIQUES Digital Image Classification Techniques group pixels to represent land cover features. Land cover could be forested, urban, agricultural and other types of features. There are three main image classification techniques. IMAGE CLASSIFICATION TECHNIQUES IN REMOTE SENSING: Unsupervised image classification Supervised image classification UNSUPERVISED IMAGE CLASSIFICATION In this, the Pixels are grouped based on the reflectance properties of pixels. These groupings are called “clusters.” In this classification technique the user manually identifies each cluster with land cover classes. It’s often the case that multiple clusters represent a single land cover class. The user merges clusters into a land cover type. The unsupervised classification image classification technique is commonly used when no sample sites exist. Unsupervised Classification Steps: Generate clusters Assign classes
- 2. Unsupervised Classification Diagram SUPERVISED CLASSIFICATION The user selects representative samples for each land cover class in the digital image. These sample land cover classes are called “training sites”. The classification of land cover is based on the spectral signature defined in the training set. The digital image classification software determines each class on what it resembles most in the training set. The common supervised classification algorithms are maximum likelihood and minimum-distance classification. Supervised Classification Steps: Select training areas Generate signature file Classify
- 3. Supervised Classification Diagram REFERENCES 1. Blaschke T, 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65 (2010) 2–16 2. Object-Based Classification vs Pixel-Based Classification: Comparitive Importance of Multi-Resolution Imagery (Robert C. Weih, Jr. and Norman D. Riggan, Jr.) 3. Multiresolution Segmentation: an optimization approachfor high quality multi-scale image segmentation (Martin Baatz & Arno Schape)