Constrained Least Squares Algorithms for Nonlinear Unmixing of Hyperspectral Imagery

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 no...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2015-03, Vol.53 (3), p.1287-1303
Hauptverfasser:	Pu, Hanye, Chen, Zhao, Wang, Bin, Xia, Wei
Format:	Artikel
Sprache:	eng
Schlagworte:	Abundance Abundance nonnegative constraint (ANC) abundance sum-to-one constraint (ASC) Algorithms bound constraint Computer simulation constrained nonlinear least squares (CNLS) Constraints Economic models hyperspectral imagery Hyperspectral imaging Joints Least squares method Linearity Materials Mathematical models nonlinear unmixing Nonlinearity Optimization structured total least squares (STLS) Vectors
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