摘要 :
Hyperspectral (HS) pansharpening aims at fusing a panchromatic (PAN) image with a hyperspectral image, generating an image with the high spatial resolution of the former and the high spectral resolution of the latter. Recently, in...
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Hyperspectral (HS) pansharpening aims at fusing a panchromatic (PAN) image with a hyperspectral image, generating an image with the high spatial resolution of the former and the high spectral resolution of the latter. Recently, in order to enhance this process, researches have combined hyperspectral unmixing with the HS fusion model, improving the fusion results . In this combined model , endmember subspace extraction is a crucial step. Traditionally, VCA and SVD are used for subspace extraction, but VCA extracts pixels in the HS image that contain impure material signatures. In this work, we use the sparse unmixing technique to extract the endmember subspace which contains pure material signatures, which may better represent the HS image. We combine sparse unmixing-based subspace extraction with the bayesian fusion model. Results indicate that the fusion algorithm using our subspace extraction method had better global performance.
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摘要 :
Hyperspectral (HS) pansharpening aims at fusing a panchromatic (PAN) image with a hyperspectral image, generating an image with the high spatial resolution of the former and the high spectral resolution of the latter. Recently, in...
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Hyperspectral (HS) pansharpening aims at fusing a panchromatic (PAN) image with a hyperspectral image, generating an image with the high spatial resolution of the former and the high spectral resolution of the latter. Recently, in order to enhance this process, researches have combined hyperspectral unmixing with the HS fusion model, improving the fusion results . In this combined model , endmember subspace extraction is a crucial step. Traditionally, VCA and SVD are used for subspace extraction, but VCA extracts pixels in the HS image that contain impure material signatures. In this work, we use the sparse unmixing technique to extract the endmember subspace which contains pure material signatures, which may better represent the HS image. We combine sparse unmixing-based subspace extraction with the bayesian fusion model. Results indicate that the fusion algorithm using our subspace extraction method had better global performance.
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