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Remote Sens. 2019, 11(2), 148; https://doi.org/10.3390/rs11020148

Parameterized Nonlinear Least Squares for Unsupervised Nonlinear Spectral Unmixing

1
College of Electrical Engineering, Zhejiang University, No.38, Zheda Road, Xihu District, Hangzhou 310027, China
2
Jiaxing Hengchuang Power Equipment Co., Ltd., Jiaxing 314000, China
3
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Received: 11 December 2018 / Revised: 2 January 2019 / Accepted: 11 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Advances in Unmixing of Spectral Imagery)
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Abstract

This paper presents a new parameterized nonlinear least squares (PNLS) algorithm for unsupervised nonlinear spectral unmixing (UNSU). The PNLS-based algorithms transform the original optimization problem with respect to the endmembers, abundances, and nonlinearity coefficients estimation into separate alternate parameterized nonlinear least squares problems. Owing to the Sigmoid parameterization, the PNLS-based algorithms are able to thoroughly relax the additional nonnegative constraint and the nonnegative constraint in the original optimization problems, which facilitates finding a solution to the optimization problems . Subsequently, we propose to solve the PNLS problems based on the Gauss–Newton method. Compared to the existing nonnegative matrix factorization (NMF)-based algorithms for UNSU, the well-designed PNLS-based algorithms have faster convergence speed and better unmixing accuracy. To verify the performance of the proposed algorithms, the PNLS-based algorithms and other state-of-the-art algorithms are applied to synthetic data generated by the Fan model and the generalized bilinear model (GBM), as well as real hyperspectral data. The results demonstrate the superiority of the PNLS-based algorithms. View Full-Text
Keywords: unsupervised nonlinear spectral unmixing; parameterized nonlinear least squares; Sigmoid parameterization; Gauss–Newton optimization unsupervised nonlinear spectral unmixing; parameterized nonlinear least squares; Sigmoid parameterization; Gauss–Newton optimization
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Huang, R.; Li, X.; Lu, H.; Li, J.; Zhao, L. Parameterized Nonlinear Least Squares for Unsupervised Nonlinear Spectral Unmixing. Remote Sens. 2019, 11, 148.

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