Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295182
Thomas Blaschke
The continuously improving spatial resolution of remote sensing sensors sets new demand for applications utilizing this information. The need for the more efficient extraction of information from high resolution RS imagery and the seamless integration of this information into Geographic Information System (GIS) databases is driving geo-information theory, and methodology, into new territory. As the dimension of the ground instantaneous field of view (GIFOV), or pixel size, decreases many more fine landscape features can be readily delineated, at least visually. The challenge has been to produce proven man-machine methods that externalize and improve on human interpretation skills. Some of the most promising results come from the adoption of image segmentation algorithms and the development of so-called object-based classification methodologies. This paper builds on a discussion of different approaches to image segmentation techniques and demonstrates through several applications how segmentation and object-based methods improve on pixel-based image analysis/classification methods. In contrast to pixel-based procedure, image objects can carry many more attributes than only spectral information. In this paper, I address the concepts of object-based image processing, and present an approach that integrates the concept of object-based processing into the image classification process. Object-based processing not only considers contextual information but also information about the shape of and the spatial relations between the image regions.
{"title":"Object-based contextual image classification built on image segmentation","authors":"Thomas Blaschke","doi":"10.1109/WARSD.2003.1295182","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295182","url":null,"abstract":"The continuously improving spatial resolution of remote sensing sensors sets new demand for applications utilizing this information. The need for the more efficient extraction of information from high resolution RS imagery and the seamless integration of this information into Geographic Information System (GIS) databases is driving geo-information theory, and methodology, into new territory. As the dimension of the ground instantaneous field of view (GIFOV), or pixel size, decreases many more fine landscape features can be readily delineated, at least visually. The challenge has been to produce proven man-machine methods that externalize and improve on human interpretation skills. Some of the most promising results come from the adoption of image segmentation algorithms and the development of so-called object-based classification methodologies. This paper builds on a discussion of different approaches to image segmentation techniques and demonstrates through several applications how segmentation and object-based methods improve on pixel-based image analysis/classification methods. In contrast to pixel-based procedure, image objects can carry many more attributes than only spectral information. In this paper, I address the concepts of object-based image processing, and present an approach that integrates the concept of object-based processing into the image classification process. Object-based processing not only considers contextual information but also information about the shape of and the spatial relations between the image regions.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129061422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295190
P. S. Huang, T. Tu
To extract GIS features from high spatial resolution imagery is an important task in remote sensing applications. However, traditional pixel-based classification methods, which were developed in the era of 10-100 m ground pixel size imagery, cannot exploit the advantages of new images provided by IKONOS and QuickBird. To successfully extract various land covers from high resolution imagery, a Target-Clustering Fusion (TCF) system is presented in this work. Compared to the conventional classification methods that typically produce more salt-and-pepper-like results, the proposed TCF system can preserve detailed spatial information on each classified target related to its neighbors.
{"title":"A target fusion-based approach for classifying high spatial resolution imagery","authors":"P. S. Huang, T. Tu","doi":"10.1109/WARSD.2003.1295190","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295190","url":null,"abstract":"To extract GIS features from high spatial resolution imagery is an important task in remote sensing applications. However, traditional pixel-based classification methods, which were developed in the era of 10-100 m ground pixel size imagery, cannot exploit the advantages of new images provided by IKONOS and QuickBird. To successfully extract various land covers from high resolution imagery, a Target-Clustering Fusion (TCF) system is presented in this work. Compared to the conventional classification methods that typically produce more salt-and-pepper-like results, the proposed TCF system can preserve detailed spatial information on each classified target related to its neighbors.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122459114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295191
Xudong Zhang, V. Vijayaraj, N. H. Younan
A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. Experimental results are presented and possible future works are discussed.
{"title":"Hyperspectral soil texture classification","authors":"Xudong Zhang, V. Vijayaraj, N. H. Younan","doi":"10.1109/WARSD.2003.1295191","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295191","url":null,"abstract":"A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. Experimental results are presented and possible future works are discussed.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129475621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295206
M. Eismann, R. Hardie
A maximum a posteriori (MAP) estimation approach to the hyperspectral resolution enhancement problem is described for enhancing the spatial resolution of a hyperspectral image using a higher resolution, coincident, panchromatic image. The approach makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to develop a cost function that simultaneously optimizes the estimated hyperspectral scene relative to the observed hyperspectral and panchromatic imagery, as well as the local statistics of the spectral mixing model. The incorporation of the stochastic mixing model is found to be the key ingredient to reconstructing subpixel spectral information in that it provides the necessary constraints that lead to a well-conditioned linear system of equations for the high resolution hyperspectral image estimate. The mathematical formulation of the method is described, and enhancement results are provided for a synthetically-generated hyperspectral image data set and compared to prior methods. In general, it is found that the MAP/SMM method is able to reconstruct sub-pixel information in several principal components of the high resolution hyperspectral image estimate, while the enhancement for conventional methods, like those based on least-squares estimation, is limited primarily to the first principal component (i.e., the intensity component).
{"title":"Resolution enhancement of hyperspectral imagery using coincident panchromatic imagery and a stochastic mixing model","authors":"M. Eismann, R. Hardie","doi":"10.1109/WARSD.2003.1295206","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295206","url":null,"abstract":"A maximum a posteriori (MAP) estimation approach to the hyperspectral resolution enhancement problem is described for enhancing the spatial resolution of a hyperspectral image using a higher resolution, coincident, panchromatic image. The approach makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to develop a cost function that simultaneously optimizes the estimated hyperspectral scene relative to the observed hyperspectral and panchromatic imagery, as well as the local statistics of the spectral mixing model. The incorporation of the stochastic mixing model is found to be the key ingredient to reconstructing subpixel spectral information in that it provides the necessary constraints that lead to a well-conditioned linear system of equations for the high resolution hyperspectral image estimate. The mathematical formulation of the method is described, and enhancement results are provided for a synthetically-generated hyperspectral image data set and compared to prior methods. In general, it is found that the MAP/SMM method is able to reconstruct sub-pixel information in several principal components of the high resolution hyperspectral image estimate, while the enhancement for conventional methods, like those based on least-squares estimation, is limited primarily to the first principal component (i.e., the intensity component).","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129756542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295219
P. Goovaerts, G. Jacquez, A. Warner, B. Crabtree, Andrew H. Marcus
This paper describes a methodology to detect local anomalies in high resolution hyperspectral imagery, which involves successively a multivariate statistical analysis (PCA) of all spectral bands, a geostatistical filtering of noise and regional background in the first principal components using factorial kriging, and finally the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values as well as anomalies. A case study illustrates the ability of the filtering procedure to reduce the proportion of false alarms, and its robustness under low signal to noise ratios. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.
{"title":"Detection of local anomalies in high resolution hyperspectral imagery using geostatistical filtering and local spatial statistics","authors":"P. Goovaerts, G. Jacquez, A. Warner, B. Crabtree, Andrew H. Marcus","doi":"10.1109/WARSD.2003.1295219","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295219","url":null,"abstract":"This paper describes a methodology to detect local anomalies in high resolution hyperspectral imagery, which involves successively a multivariate statistical analysis (PCA) of all spectral bands, a geostatistical filtering of noise and regional background in the first principal components using factorial kriging, and finally the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values as well as anomalies. A case study illustrates the ability of the filtering procedure to reduce the proportion of false alarms, and its robustness under low signal to noise ratios. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130744166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295212
P. Mantero, G. Moser, S. Serpico
A general problem of supervised remotely. sensed image classification assumes prior knowledge to be available for all thematic classes that are present in the considered data set. However, the ground truth map representing this prior knowledge usually does not really, describe all the land cover typologies in the image and the generation of a complete training set represents a time-consuming, difficult and expensive task. This problem may play a relevant role in remote sensing data analysis, since it affects the classification performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is proposed, which allows the identification of samples drawn from unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density, functions and on a recursive procedure to generate prior probabilities estimates for both known and unknown classes. For experimental purposes, both a synthetic data set and two real data sets are employed.
{"title":"Partially supervised classification of remote sensing images using SVM-based probability density estimation","authors":"P. Mantero, G. Moser, S. Serpico","doi":"10.1109/WARSD.2003.1295212","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295212","url":null,"abstract":"A general problem of supervised remotely. sensed image classification assumes prior knowledge to be available for all thematic classes that are present in the considered data set. However, the ground truth map representing this prior knowledge usually does not really, describe all the land cover typologies in the image and the generation of a complete training set represents a time-consuming, difficult and expensive task. This problem may play a relevant role in remote sensing data analysis, since it affects the classification performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is proposed, which allows the identification of samples drawn from unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density, functions and on a recursive procedure to generate prior probabilities estimates for both known and unknown classes. For experimental purposes, both a synthetic data set and two real data sets are employed.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131319194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295192
M. Faryś
The objective of the presented research was to study influence of soil salinity on soil bidirectional reflectance in the optical domain. Soils in two zones were selected for the examination: Ex-Lago Texoco located north east from Mexico City, Mexico State and area of Lake Cuitzeo north of Morelia, Michoacan State. Bidirectional reflectance was measured by the GER radiometer. Results were presented by normalized reflectance as a function of view zenithal angle depending on the solar azimuth angle. Soils carrying salt on their surface have a different bidirectional characteristic in comparison with soils not containing salt or soils that are subject to tillage practice. Bidirectional reflectance of this soil is similar to specular reflection. In this paper, it is shown that the forwardscatter regime is demonstrated in the bidirectional reflectance of a soil surface composed of salt.
{"title":"Influence of soil salinity on soil bidirectional reflectance in optical domain","authors":"M. Faryś","doi":"10.1109/WARSD.2003.1295192","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295192","url":null,"abstract":"The objective of the presented research was to study influence of soil salinity on soil bidirectional reflectance in the optical domain. Soils in two zones were selected for the examination: Ex-Lago Texoco located north east from Mexico City, Mexico State and area of Lake Cuitzeo north of Morelia, Michoacan State. Bidirectional reflectance was measured by the GER radiometer. Results were presented by normalized reflectance as a function of view zenithal angle depending on the solar azimuth angle. Soils carrying salt on their surface have a different bidirectional characteristic in comparison with soils not containing salt or soils that are subject to tillage practice. Bidirectional reflectance of this soil is similar to specular reflection. In this paper, it is shown that the forwardscatter regime is demonstrated in the bidirectional reflectance of a soil surface composed of salt.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124269759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295218
D. Manolakis
The purpose of this paper is to present a unified, simplified, and concise, overview of spectral target detection algorithms for hyperspectral imaging applications. We focus on detection algorithms derived using established statistical techniques and whose performance is predictable under reasonable assumptions about hyperspectral imaging data. The emphasis on a signal processing perspective helps to, better understand the strengths and limitations of each algorithm, avoid unrealistic performance expectations, and apply an algorithm properly and sensibly.
{"title":"Detection algorithms for hyperspectral imaging applications: a signal processing perspective","authors":"D. Manolakis","doi":"10.1109/WARSD.2003.1295218","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295218","url":null,"abstract":"The purpose of this paper is to present a unified, simplified, and concise, overview of spectral target detection algorithms for hyperspectral imaging applications. We focus on detection algorithms derived using established statistical techniques and whose performance is predictable under reasonable assumptions about hyperspectral imaging data. The emphasis on a signal processing perspective helps to, better understand the strengths and limitations of each algorithm, avoid unrealistic performance expectations, and apply an algorithm properly and sensibly.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123892012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295180
M. Chiarella, D. Fay, A. Waxman, R. Ivey, N. Bomberger
We summarize our methods for the fusion of multisensor/spectral imagery based on concepts derived from neural models of visual processing (adaptive contrast enhancement, opponent-color contrast, multi-scale contour completion, and multi-scale texture enhancement) and semi-supervised pattern learning and recognition. These methods have been applied to the problem of aided feature extraction (AFE) from remote sensing airborne multispectral and hyperspectral imaging systems, and space-based multi-platform multi-modality imaging sensors. The methods enable color fused 3D visualization, as well as interactive exploitation and data mining in the form of human-guided machine learning and search for objects, landcover, and cultural features. This technology has been evaluated on space-based imagery for the National Imagery and Mapping Agency, and real-time implementation has also been demonstrated for terrestrial fused-color night imaging. We have recently incorporated these methods into a commercial software platform (ERDAS Imagine) for imagery exploitation. We describe the approach and user interfaces, and show results for a variety of sensor systems with application to remote sensing feature extraction including EO/IR/MSI/SAR imagery from Landsat and Radarsat, multispectral Ikonos imagery, and Hyperion and HyMap hyperspectral imagery.
{"title":"Multisensor image fusion and mining: from neural systems to COTS software with application to remote sensing AFE","authors":"M. Chiarella, D. Fay, A. Waxman, R. Ivey, N. Bomberger","doi":"10.1109/WARSD.2003.1295180","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295180","url":null,"abstract":"We summarize our methods for the fusion of multisensor/spectral imagery based on concepts derived from neural models of visual processing (adaptive contrast enhancement, opponent-color contrast, multi-scale contour completion, and multi-scale texture enhancement) and semi-supervised pattern learning and recognition. These methods have been applied to the problem of aided feature extraction (AFE) from remote sensing airborne multispectral and hyperspectral imaging systems, and space-based multi-platform multi-modality imaging sensors. The methods enable color fused 3D visualization, as well as interactive exploitation and data mining in the form of human-guided machine learning and search for objects, landcover, and cultural features. This technology has been evaluated on space-based imagery for the National Imagery and Mapping Agency, and real-time implementation has also been demonstrated for terrestrial fused-color night imaging. We have recently incorporated these methods into a commercial software platform (ERDAS Imagine) for imagery exploitation. We describe the approach and user interfaces, and show results for a variety of sensor systems with application to remote sensing feature extraction including EO/IR/MSI/SAR imagery from Landsat and Radarsat, multispectral Ikonos imagery, and Hyperion and HyMap hyperspectral imagery.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295168
R. King
Outcomes from the analysis of remote sensing imagery are often used in making important decisions. These decisions may have an impact on national security, the establishment of protocols regulating the emission of greenhouse gases, the selection of a corridor for a new highway, global yield of agricultural products, etc. The objective of this paper is to provide a framework in which decisions are made to benefit society from the use of synoptic observations. First, the paper will provide a perspective on decision-making. This is necessary to be able to properly understand where the analysis techniques discussed in the workshop fit into the proposed framework. Next, the paper proposes a taxonomy that defines the link between a particular mathematical analysis technique and the higher level outcome that results from this analysis. This workshop is dedicated to David Landgrebe and an international cadre of image analysts who have pioneered new approaches to the analysis of remote sensing observations - statistical parameter estimation, multispectral and hyperspectral classification, multitemporal analysis, artificial neural network architectures and learning algorithms, among others. It is important to understand that these analysis techniques are necessary in reducing the uncertainties associated with decision-making - that remote sensing analysis is an important step in the decision-making process that has its beginning in the technology used to collect photons and culminates in a host of decision-support tools.
{"title":"Putting information into the service of decision making: the role of remote sensing analysis","authors":"R. King","doi":"10.1109/WARSD.2003.1295168","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295168","url":null,"abstract":"Outcomes from the analysis of remote sensing imagery are often used in making important decisions. These decisions may have an impact on national security, the establishment of protocols regulating the emission of greenhouse gases, the selection of a corridor for a new highway, global yield of agricultural products, etc. The objective of this paper is to provide a framework in which decisions are made to benefit society from the use of synoptic observations. First, the paper will provide a perspective on decision-making. This is necessary to be able to properly understand where the analysis techniques discussed in the workshop fit into the proposed framework. Next, the paper proposes a taxonomy that defines the link between a particular mathematical analysis technique and the higher level outcome that results from this analysis. This workshop is dedicated to David Landgrebe and an international cadre of image analysts who have pioneered new approaches to the analysis of remote sensing observations - statistical parameter estimation, multispectral and hyperspectral classification, multitemporal analysis, artificial neural network architectures and learning algorithms, among others. It is important to understand that these analysis techniques are necessary in reducing the uncertainties associated with decision-making - that remote sensing analysis is an important step in the decision-making process that has its beginning in the technology used to collect photons and culminates in a host of decision-support tools.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123700119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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