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With the development of imaging technology, remote sensing images with a high spatial and spectral resolution have become available and have been used in various applications such as the identification of materials and the estimation of physical parameters. Although many endmember extraction algorithms have been proposed for hyperspectral data sets which extract/select the standard endmember spectrum...
In this manuscript, we propose a sparse self-representation (SSR) method to select a band subset in hyperspectral imagery (HSI) classification. The SSR method improves from multiple measurement vectors (MMV) with the measurement matrix equals to the observation matrix. The SSR regards that each band could be represented as a linear combination of the representatives of all bands and accordingly all...
In recent years, several kinds of endmember extraction algorithms have been proposed from hyperspectral data set which extracts/selects one single standard endmember spectrum for each existing endmember class or scene component. In this article, endmember variability is considered to the mixture spectrum analysis by representing each endmember by a set or a bundle of spectra. Our method contains four...
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