Hyperspectral unmixing method for estimating regularized parameter automatically

Abstract

The invention provides an L-curve-based hyperspectral unmixing method for estimating a regularized parameter automatically. An expected target is reached by using the method in hyperspectral unmixing. The method comprises the following steps: (1) reading hyperspectral remote sensing image data V with a computer; (2) determining the number of end members in the hyperspectral image by a virtual dimensionality (CD) method; (3) initializing an end member matrix W and an abundance matrix H by a vertex component analysis (VCA) algorithm, and performing projection operations on the W and the H; and (4) determining a regularized item, determining an iteration expression of the regulated parameter alpha, the end member matrix W and the abundance matrix H, and iterating the alpha, the W and the H until simultaneous constringency of a target function and the regularized parameter. The algorithm is characterized by overcoming the unreasonable disadvantages caused by selecting the regularized parameter manually in an NMF in the prior art, utilizing a fixed point algorithm to estimate an optimal regularized parameter, and dynamically weighing a fitting degree function and the regularized item automatically so as to achieve a better unmixing effect.

Description

Technical field: [0001] The invention provides a hyperspectral unmixing method for automatically estimating regularization parameters, belonging to the field of hyperspectral remote sensing image unmixing. Background technique: [0002] In the past three decades, with the continuous development of imaging spectroscopy technology, remote sensing images collected by imaging spectrometers mounted on aircraft or satellite platforms contain more and more abundant Space, radiation and spectral information, thus providing a powerful means for information extraction and target detection of surface materials. As an important indicator of the development of remote sensing technology, spectral resolution has been developed from multi-spectral to hyperspectral, and is developing towards ultra-hyperspectral. [0003] Due to the low spatial resolution limitation of remote sensing detection instruments and the complex diversity of natural objects, the spectrum obtained at a single pixel p...

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Problems solved by technology

[0016] Existing literature (Sen Jia and Yuntao Qian. Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing[J].IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, JANUARY 2009, VOL.47, NO.1: 161-173.) has added a large number of solutions to the algorithm The regularization term, but the regularization parameters need to be given artificially, which is unreasonable to a large extent

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