On Diverse Noises in Hyperspectral Unmixing

Traditional spectral unmixing methods are usually based on the linear mixture model (LMM) or nonlinear mixture model (NLMM), in which only the additive noise is considered. However, in hyperspectral applications, the additive, multiplicative, and mixed noises play important roles. In this paper, we...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2015-10, Vol.53 (10), p.5388-5402
Hauptverfasser:	Chunzhi Li, Xiaohua Chen, Yunliang Jiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Additive noise Additives Antinoise model Hyperspectral imaging Itakura-Saito (IS) divergence Matching pursuit algorithms multiplicative noise random measure errors Robustness spectral unmixing (SU)
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