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Spectral unmixing is an important technique to exploit mineral distribution through remote sensing image. In this paper, we propose an unmixing algorithm combining clustering-aware method with the sparsity-constrained nonnegative matrix factorization (SNMF) algorithm. Pixels with similar spectra have high possibility to share similar typical endmembers, therefore we preprocess the image using K-means...
This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed...
Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematically...
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