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Hyperspectral image classification based on low-rank representation is considered. It is often assumed that major signals occupy a low-rank subspace, and the remaining component is sparse. Due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose to use low-rank subspace representation...
Endmembers play an important role in many hyperspectral remote sensing applications, such as classification and Spectral Mixture Unmixing (SMU). In this paper, by considering endmembers as a small subset of pixels in a hyperspectral image, a sparse Linear Mixture Model (sLMM) is constructed to model the mixed pixels. As a result, an L2,0 based sparse dictionary selection model is proposed for endmember...
Hyperspectral image dimensionality reduction with graph-based approaches is considered. With available labeled samples, a graph can be formed with these samples by constructing an affinity matrix through their sparse or collaborative representations. In addition, sparse or collaborative representation can be done using within-class samples, resulting in block-sparse representation, although within...
Sparse unmixing has been proposed for hyperspectral image analysis. It has been shown that improved performance can be achieved when endmembers from a spectral library are used. However, when endmembers from image data have to be employed for unmixing, such a sparse-constrained approach may be problematic due to the fact that endmembers are generally highly coherent, thereby producing unstable sparse...
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