A Spectral and Spatial Source Separation of Multispectral Images
This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned...
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Published in | IEEE transactions on geoscience and remote sensing Vol. 44; no. 12; pp. 3659 - 3673 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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New York, NY
IEEE
01.12.2006
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned zone constitute the sources to separate. Associating each source to a specific significant theme remains the real challenge in the source-separation method applied to satellite images. In fact, multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. This constitutes the first difficulty for sources identification. The second difficulty lies in the heterogeneity of most of the geological and vegetative ground surfaces. In this case, the geometrical projection of a single detector element at the Earth's surface, which is sometimes called the instantaneous field of view, is formed from a mixture of spectral signatures. In such circumstances, the needed information is either not available or not reliable. In this paper, the goal is to establish a new approach based on a two-level source separation (TLSS), which consists of a spectral separation along the different used bands and a spatial separation along neighboring pixels of each image band. The spectral separation has been used prior to the Bayesian approach, and it is based on a second-order statistics approach that exploits the correlation through different spectral bands of the multispectral sensor. The given images are represented according to independent axes that provide more effective representation of the information within the observation images. The spectral separation consists of identifying the sources without resorting to any a priori information, hence the term blind. The obtained sources represent the starting point for the Bayesian approach, which is known for its weakness in front of initial conditions. To identify a significant theme for each source, we have to spatially separate each image based on a Bayesian source-separation framework. The proposed approach has the added advantages of the blind source method as well as the Bayesian method. It should give segmented images related to each theme covering the scanned zone, which are the TLSS results of the observation images |
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AbstractList | This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned zone constitute the sources to separate. Associating each source to a specific significant theme remains the real challenge in the source-separation method applied to satellite images. In fact, multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. This constitutes the first difficulty for sources identification. The second difficulty lies in the heterogeneity of most of the geological and vegetative ground surfaces. In this case, the geometrical projection of a single detector element at the Earth's surface, which is sometimes called the instantaneous field of view, is formed from a mixture of spectral signatures. In such circumstances, the needed information is either not available or not reliable. In this paper, the goal is to establish a new approach based on a two-level source separation (TLSS), which consists of a spectral separation along the different used bands and a spatial separation along neighboring pixels of each image band. The spectral separation has been used prior to the Bayesian approach, and it is based on a second-order statistics approach that exploits the correlation through different spectral bands of the multispectral sensor. The given images are represented according to independent axes that provide more effective representation of the information within the observation images. The spectral separation consists of identifying the sources without resorting to any a priori information, hence the term blind. The obtained source- - s represent the starting point for the Bayesian approach, which is known for its weakness in front of initial conditions. To identify a significant theme for each source, we have to spatially separate each image based on a Bayesian source-separation framework. The proposed approach has the added advantages of the blind source method as well as the Bayesian method. It should give segmented images related to each theme covering the scanned zone, which are the TLSS results of the observation images This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned zone constitute the sources to separate. Associating each source to a specific significant theme remains the real challenge in the source-separation method applied to satellite images. In fact, multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. This constitutes the first difficulty for sources identification. The second difficulty lies in the heterogeneity of most of the geological and vegetative ground surfaces. In this case, the geometrical projection of a single detector element at the Earth's surface, which is sometimes called the instantaneous field of view, is formed from a mixture of spectral signatures. In such circumstances, the needed information is either not available or not reliable. In this paper, the goal is to establish a new approach based on a two-level source separation (TLSS), which consists of a spectral separation along the different used bands and a spatial separation along neighboring pixels of each image band. The spectral separation has been used prior to the Bayesian approach, and it is based on a second-order statistics approach that exploits the correlation through different spectral bands of the multispectral sensor. The given images are represented according to independent axes that provide more effective representation of the information within the observation images. The spectral separation consists of identifying the sources without resorting to any a priori information, hence the term blind. The obtained sources represent the starting point for the Bayesian approach, which is known for its weakness in front of initial conditions. To identify a significant theme for each source, we have to spatially separate each image based on a Bayesian source-separation framework. The proposed approach has the added advantages of the blind source method as well as the Bayesian method. It should give segmented images related to each theme covering the scanned zone, which are the TLSS results of the observation images [...] multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. |
Author | Naceur, M.S. Loghmari, M.A. Boussema, M.R. |
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Keywords | bayesian analysis Bayesian estimation multispectral remote sensing Space remote sensing classification heterogeneity frequency spectral signature multispectral image classification channels correlation separation projection blind source separation land cover Pixel energy statistics |
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References | ref13 ref15 snoussi (ref28) 2000 ref31 ref30 ref11 ref10 comon (ref18) 1988; 5 ref2 ref17 idier (ref29) 2000 ref16 ref19 lennon (ref9) 2003 nuzillard (ref7) 2000 horwitz (ref12) 1971 makeig (ref5) 1995; 8 ref24 ref26 ref25 ref20 ref22 mansour (ref1) 2000; e83 a ref21 ref8 bermond (ref27) 1999 ref4 campbell (ref14) 1996 chaumette (ref3) 1993; 140 ref6 belouchrani (ref23) 1993 |
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SubjectTerms | Applied geophysics Band spectra Bandwidth Bayesian analysis Bayesian estimation Bayesian methods Blind source separation Blinds Channels Detectors Earth sciences Earth, ocean, space Exact sciences and technology Frequency Geology Internal geophysics multispectral image classification Multispectral imaging Remote sensing Satellites Separation Source separation Spectra Spectral bands Studies |
Title | A Spectral and Spatial Source Separation of Multispectral Images |
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