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.
Conferencia impartida por Sebastián Sánchez en los Viernes Científicos organizada por la Facultad de Ciencias Experimentales de la Universidad de Almería el 14 de enero de 2011.
Evaluating effectiveness of radiometric correction for optical satellite imag...Dang Le
One of our published researches in ACRS 31st in Hanoi.
It has been used for our project in processing optical satellite imagery to detect environmental pollution.
Conferencia impartida por Sebastián Sánchez en los Viernes Científicos organizada por la Facultad de Ciencias Experimentales de la Universidad de Almería el 14 de enero de 2011.
Evaluating effectiveness of radiometric correction for optical satellite imag...Dang Le
One of our published researches in ACRS 31st in Hanoi.
It has been used for our project in processing optical satellite imagery to detect environmental pollution.
A ~25 slide presentation that explains the underlying principles and some applications of InSAR, with a particular focus on the measurement of deformation due to earthquakes. The presentation could be used in a lecture or lab setting, or provided to students for review out of class. The slides are annotated with additional background information designed to assist instructors.
SAUSAGE MINKOWSKI SQUARE PATCH ANTENNA FOR GPS APPLICATIONP singh
Square Patch Antennas find wide range in practical modern communication system. The novel idea employed in this paper is to use Sausage Minkowski of square patch antenna as a candidate for using in Global Positioning System (GPS) applications. The antenna has been designed to operate at frequency resonance of 1.575 GHz which is the upper GPS band (L1). The proposed antenna is designed and simulated using CST Studio suite version-2014. The proposed antenna has been simulated on substrate TMM4 of relative permittivity equals to 4.5 and thickness of 1.6 mm. The results are reported in terms of reflection coefficient, VSWR, directivity and gain. According to our results, the antenna at 2nd iteration has gain 4.61dB, VSWR is 1.2 and reduce in area of patch about 42.8% less than 0 iteration. The antenna is more efficient by applying fractal geometry.
Extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather in the observed record (satellite, reanalysis products) and characterizing changes in extremes in simulations of future climate regimes is an important task. Thus far, extreme weather events have been typically specified by the community through hand-coded, multi-variate threshold conditions. Such criteria are usually subjective, and often there is little agreement in the community on the specific algorithm that should be used. We propose the use of a different approach: machine learning (and in particular deep learning) for solving this important problem. If human experts can provide spatio-temporal patches of a climate dataset, and associated labels, we can turn to a machine learning system to learn the underlying feature representation. The trained Machine Learning (ML) system can then be applied to novel datasets, thereby automating the pattern detection step. Summary statistics, such as location, intensity and frequency of such events can be easily computed as a post-process.
We will report compelling results from our investigations of Deep Learning for the tasks of classifying tropical cyclones, atmospheric rivers and weather front events. For all of these events, we observe 90-99% classification accuracy. We will also report on progress in localizing such events: namely drawing a bounding box (of the correct size and scale) around the weather pattern of interest. Both tasks currently utilize multi-layer convolutional networks in conjunction with hyper-parameter optimization. We utilize HPC systems at NERSC to perform the optimization across multiple nodes, and utilize highly-tuned libraries to utilize multiple cores on a single node. We will conclude with thoughts on the frontier of Deep Learning and the role of humans (vis-a-vis AI) in the scientific discovery process.
Mapping fire: Can spatially explicit criteria and indicators be developed?CIFOR-ICRAF
Presented by Solichin Manuri, Senior Advisor at Diameter Consulting, Bogor, Indonesia, at "Online Webinar 2: Biophysical Attributes and Peatland Fires", on 14 October 2020
In this session the speaker shared information on mapping fire (extent and occurrences) in tropical peatlands including in Indonesia. This session also shared insights on the existing methods that can be used for fire mapping and comparisons. This session also emphasized that spatial explicit criteria for fire should be developed depending on the method and data used.
The muse 3_d_view_of_the_hubble_deep_field_southSérgio Sacani
Artigo mostra como foram as observações feitas com o MUSE, o novo instrumento do VLT do campo profundo do Hubble. Além de descobrir 20 novos objetos, o MUSE conseguiu medir as propriedades das galáxias e até representar as mais próximas em 3 dimensões.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...ijaia
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
A ~25 slide presentation that explains the underlying principles and some applications of InSAR, with a particular focus on the measurement of deformation due to earthquakes. The presentation could be used in a lecture or lab setting, or provided to students for review out of class. The slides are annotated with additional background information designed to assist instructors.
SAUSAGE MINKOWSKI SQUARE PATCH ANTENNA FOR GPS APPLICATIONP singh
Square Patch Antennas find wide range in practical modern communication system. The novel idea employed in this paper is to use Sausage Minkowski of square patch antenna as a candidate for using in Global Positioning System (GPS) applications. The antenna has been designed to operate at frequency resonance of 1.575 GHz which is the upper GPS band (L1). The proposed antenna is designed and simulated using CST Studio suite version-2014. The proposed antenna has been simulated on substrate TMM4 of relative permittivity equals to 4.5 and thickness of 1.6 mm. The results are reported in terms of reflection coefficient, VSWR, directivity and gain. According to our results, the antenna at 2nd iteration has gain 4.61dB, VSWR is 1.2 and reduce in area of patch about 42.8% less than 0 iteration. The antenna is more efficient by applying fractal geometry.
Extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather in the observed record (satellite, reanalysis products) and characterizing changes in extremes in simulations of future climate regimes is an important task. Thus far, extreme weather events have been typically specified by the community through hand-coded, multi-variate threshold conditions. Such criteria are usually subjective, and often there is little agreement in the community on the specific algorithm that should be used. We propose the use of a different approach: machine learning (and in particular deep learning) for solving this important problem. If human experts can provide spatio-temporal patches of a climate dataset, and associated labels, we can turn to a machine learning system to learn the underlying feature representation. The trained Machine Learning (ML) system can then be applied to novel datasets, thereby automating the pattern detection step. Summary statistics, such as location, intensity and frequency of such events can be easily computed as a post-process.
We will report compelling results from our investigations of Deep Learning for the tasks of classifying tropical cyclones, atmospheric rivers and weather front events. For all of these events, we observe 90-99% classification accuracy. We will also report on progress in localizing such events: namely drawing a bounding box (of the correct size and scale) around the weather pattern of interest. Both tasks currently utilize multi-layer convolutional networks in conjunction with hyper-parameter optimization. We utilize HPC systems at NERSC to perform the optimization across multiple nodes, and utilize highly-tuned libraries to utilize multiple cores on a single node. We will conclude with thoughts on the frontier of Deep Learning and the role of humans (vis-a-vis AI) in the scientific discovery process.
Mapping fire: Can spatially explicit criteria and indicators be developed?CIFOR-ICRAF
Presented by Solichin Manuri, Senior Advisor at Diameter Consulting, Bogor, Indonesia, at "Online Webinar 2: Biophysical Attributes and Peatland Fires", on 14 October 2020
In this session the speaker shared information on mapping fire (extent and occurrences) in tropical peatlands including in Indonesia. This session also shared insights on the existing methods that can be used for fire mapping and comparisons. This session also emphasized that spatial explicit criteria for fire should be developed depending on the method and data used.
The muse 3_d_view_of_the_hubble_deep_field_southSérgio Sacani
Artigo mostra como foram as observações feitas com o MUSE, o novo instrumento do VLT do campo profundo do Hubble. Além de descobrir 20 novos objetos, o MUSE conseguiu medir as propriedades das galáxias e até representar as mais próximas em 3 dimensões.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...ijaia
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
Multispectral images are used for space Arial application, target detection and remote sensing application. MS images are very rich in spectral resolution but at a cost of spatial resolution. We propose a new method to increase a spatial resolution MS images. For spatial resolution enhancement of MS images we need to employ a super-resolution technique which uses a Principal Component Analysis (PCA) based approach by learning an edge details from database. Experiments have been carried out on both real multispectral (MS) data and MS data. This experiment is done with the usefulness for hyper spectral (HS) data as a future work.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
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.
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.
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.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION acijjournal
ABSTRACT
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.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSIONacijjournal
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.
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...IJECEIAES
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.
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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
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.
25.
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.
28.
29.
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
33.
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
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
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.
![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-201111-110725155720-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)