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The minimization problem of reconstruction error over large hyperspectral image data is one of the most important problems in unsupervised hyperspectral unmixing. A variety of algorithms based on nonnegative matrix factorization (NMF) have been proposed in the literature to solve this minimization problem. One popular optimization method for NMF is the projected gradient descent (PGD). However, as...
Most nonlinear unmixing algorithms are based on the nonlinear mixing models with different forms. This paper focuses on the well-known generalized bilinear model (GBM). Though the GBM has shown interesting and promising for nonlinear unmixing, currently almost all the GBM-based unmixing algorithms are supervised. That is, the endmembers must be assumed known in advance. This paper develops an unsupervised...
Aiming at the high accuracy and speed requirements of images registration for multiband data or hyperspectral data, a new method which combines scale invariant feature transform (SIFT) with vegetation index analysis is put forward. Firstly, feature points extracted by SIFT algorithm are classified into two sets — points on vegetation area and points on non-vegetation area, which is based on vegetation...
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