The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
Curtis E. Woodcock - One of the best experts on this subject based on the ideXlab platform.
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Rationale and conceptual framework for classification approaches to assess forest resources and properties
Remote Sensing of Forest Environments, 2003Co-Authors: Janet Franklin, John Rogan, Stuart R. Phinn, Curtis E. WoodcockAbstract:Classification has been an important tool in digital image analysis for land resources applications since early Landsat missions when it was recognized that multispectral digital images are composed of multivariate measurement vectors for each and every pixel. The hundreds of thousands of such vectors typically making up an image could be treated as class descriptors, and the spectral bands as explanatory variables related to categories of interest in the image. This is an application of the more general methodology of classification or pattern recognition (Ripley 1996). This Chapter provides a conceptual framework for selecting appropriate classification approaches to assess forest resources and forest (canopy, stand, and landscape) properties. It is beyond the scope of this Chapter to provide a comprehensive review of recent literature on image classification. S. E. Franklin (2001) provided an excellent overview of classification for the remote sensing of forests, and we use that work as a point of departure. Textbooks such as those by Jensen (1996) and Schowengerdt (1997) provide comprehensive explanations of the general problem of classification in remote sensing. Several recent reviews also outline advances in the use of classification in forest remote sensing Wulder 1998, Trietz and Howarth 1999, Lucas et al. in press, Woodcock in press).
Mohammad Reza Saradjian - One of the best experts on this subject based on the ideXlab platform.
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quality assessment of pan sharpening methods in high resolution satellite images using radiometric and geometric index
Arabian Journal of Geosciences, 2016Co-Authors: Mahdi Hasanlou, Mohammad Reza SaradjianAbstract:This paper focuses on quality assessment of fusion of multispectral (MS) images with high-resolution panchromatic (Pan) images. Since most existing quality assessments take the entire image into account simultaneously and generate some uncertainties, a novel and rather objective quality index has been proposed for image fusion. The index is comprised of geometric and radiometric parts. Both geometric and radiometric measurements are calculated using morphological algorithm applied on an edge image to create a mask which is used to separate high-frequency regions from low-frequency ones. The accuracy assessment is made using common existing criteria on geometric and radiometric segments, and then a weighted sum is calculated to generate radiometric and geometric index (RG index). Several commonly used fusion algorithms such as IHS, modified IHS, PCA, Gram-Schmidt, Brovey Transform, Ehlers, High-Pass Modulation, Schowengerdt and UNB were applied on a very high-resolution GeoEye-1 and WorldView-2 images. In order to perform quality assessment, methods of Spectral Angle Mapper, Structural SIMilarity, correlation coefficients and universal quality index for which the normalization were possible (for comparison purposes) were used. The utilized RG index showed that by separating spectral and spatial component quality measurement, the quality assessment is made on fused images in a more distinct, explicit, accurate and objective manner.
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IGARSS - Satellite image fusion quality measurement by separating radiometric and geometric components
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Hasanlou, Mohammad Reza Saradjian, Farhad SamadzadeganAbstract:This paper focuses on quality assessment of fusion of multispectral (MS) images with high-resolution panchromatic (Pan) images. Since most existing quality assessments take the entire image into account simultaneously and generate some uncertainties, a novel and rather objective quality index has been proposed for image fusion. The index is comprised of geometric and radiometric parts. Both geometric and radiometric measurements are calculated using morphological algorithm applied on an edge image to create a mask which is used to separate high frequency regions from low frequency ones. The accuracy assessment is made using common existing criteria on geometric and radiometric segments and then a weighted sum is calculated to generate Radiometric and Geometric index (RG index). Several commonly used fusion algorithms such as IHS, modified IHS, PCA, Gram-Schmidt, Brovey Transform, Ehlers, High-Pass Modulation, Schowengerdt and UNB were applied on a very high resolution GeoEye and WorldView-2 images. In order to perform quality assessment, methods of Spectral Angle Mapper, Structural SIMilarity, Correlation Coefficients and Universal Quality Index for which the normalization were possible (for comparison purposes) were used. The utilized RG index showed that by separating spectral and spatial component quality measurement, the quality assessment is made on fused images in more distinct, explicit, accurate and objective manner.
Janet Franklin - One of the best experts on this subject based on the ideXlab platform.
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Rationale and conceptual framework for classification approaches to assess forest resources and properties
Remote Sensing of Forest Environments, 2003Co-Authors: Janet Franklin, John Rogan, Stuart R. Phinn, Curtis E. WoodcockAbstract:Classification has been an important tool in digital image analysis for land resources applications since early Landsat missions when it was recognized that multispectral digital images are composed of multivariate measurement vectors for each and every pixel. The hundreds of thousands of such vectors typically making up an image could be treated as class descriptors, and the spectral bands as explanatory variables related to categories of interest in the image. This is an application of the more general methodology of classification or pattern recognition (Ripley 1996). This Chapter provides a conceptual framework for selecting appropriate classification approaches to assess forest resources and forest (canopy, stand, and landscape) properties. It is beyond the scope of this Chapter to provide a comprehensive review of recent literature on image classification. S. E. Franklin (2001) provided an excellent overview of classification for the remote sensing of forests, and we use that work as a point of departure. Textbooks such as those by Jensen (1996) and Schowengerdt (1997) provide comprehensive explanations of the general problem of classification in remote sensing. Several recent reviews also outline advances in the use of classification in forest remote sensing Wulder 1998, Trietz and Howarth 1999, Lucas et al. in press, Woodcock in press).
Farhad Samadzadegan - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Satellite image fusion quality measurement by separating radiometric and geometric components
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Hasanlou, Mohammad Reza Saradjian, Farhad SamadzadeganAbstract:This paper focuses on quality assessment of fusion of multispectral (MS) images with high-resolution panchromatic (Pan) images. Since most existing quality assessments take the entire image into account simultaneously and generate some uncertainties, a novel and rather objective quality index has been proposed for image fusion. The index is comprised of geometric and radiometric parts. Both geometric and radiometric measurements are calculated using morphological algorithm applied on an edge image to create a mask which is used to separate high frequency regions from low frequency ones. The accuracy assessment is made using common existing criteria on geometric and radiometric segments and then a weighted sum is calculated to generate Radiometric and Geometric index (RG index). Several commonly used fusion algorithms such as IHS, modified IHS, PCA, Gram-Schmidt, Brovey Transform, Ehlers, High-Pass Modulation, Schowengerdt and UNB were applied on a very high resolution GeoEye and WorldView-2 images. In order to perform quality assessment, methods of Spectral Angle Mapper, Structural SIMilarity, Correlation Coefficients and Universal Quality Index for which the normalization were possible (for comparison purposes) were used. The utilized RG index showed that by separating spectral and spatial component quality measurement, the quality assessment is made on fused images in more distinct, explicit, accurate and objective manner.
Stuart R. Phinn - One of the best experts on this subject based on the ideXlab platform.
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Rationale and conceptual framework for classification approaches to assess forest resources and properties
Remote Sensing of Forest Environments, 2003Co-Authors: Janet Franklin, John Rogan, Stuart R. Phinn, Curtis E. WoodcockAbstract:Classification has been an important tool in digital image analysis for land resources applications since early Landsat missions when it was recognized that multispectral digital images are composed of multivariate measurement vectors for each and every pixel. The hundreds of thousands of such vectors typically making up an image could be treated as class descriptors, and the spectral bands as explanatory variables related to categories of interest in the image. This is an application of the more general methodology of classification or pattern recognition (Ripley 1996). This Chapter provides a conceptual framework for selecting appropriate classification approaches to assess forest resources and forest (canopy, stand, and landscape) properties. It is beyond the scope of this Chapter to provide a comprehensive review of recent literature on image classification. S. E. Franklin (2001) provided an excellent overview of classification for the remote sensing of forests, and we use that work as a point of departure. Textbooks such as those by Jensen (1996) and Schowengerdt (1997) provide comprehensive explanations of the general problem of classification in remote sensing. Several recent reviews also outline advances in the use of classification in forest remote sensing Wulder 1998, Trietz and Howarth 1999, Lucas et al. in press, Woodcock in press).