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Sparse representation has been introduced to tackle the target detection problem in hyperspectral imagery. While using windows to build the sparse dictionary, there exists target contamination problem. In our approach, we utilize a learning method based on convex optimization to build a dictionary for sparse target detection. Through its application, prior information such as the size of windows can...
Hyperspectral unmixing is an important issue in hyperspectral image processing. In this paper, we transform the unmixing problem into a constrained nonlinear least squares (CNLS) problem by introducing the abundance sum-to-one constraint, abundance nonnegative constraint, and bound constraints on nonlinearity parameters. The new CNLS-based algorithms assume that the mixing mechanism of each observed...
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