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The N-FINDR algorithm has been widely used in hyperspectral image analysis for endmember extraction due to its simplicity and effectiveness. However, there are several disadvantages of implementing the N-FINDR. This letter proposes an algorithm for decomposition of mixed pixels. It improves the N-FINDR in several aspects. First, an iterative Gram-Schmidt orthogonalization is applied in the endmember...
A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It extracts endmembers sequentially by adding a new vertex of the simplex with the maximum volume every time. It can avoid the dilemma in traditional simplex-based endmember extraction algorithms such as N-FINDR that it generally produces different sets of final endmembers if different...
The simplex-based methods are a kind of the most important and widely used methods for the decomposition of mixed pixels in multispectral and hyperspectral remote sensing images, which need a strong basic hypothesis that there is at least one pure pixel for every endmember existing in the images. N- FINDR is of typical sense in this kind of methods, and it also needs this basic hypothesis. Unfortunately,...
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