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Hyperspectral image nonlinear abundance estimation method based on constrained least squares

A hyperspectral image, least squares technology, applied in the field of remote sensing image processing, can solve problems such as inaccurate results, and achieve the effects of good adaptability, low computational complexity, good unmixing accuracy and anti-noise performance

Inactive Publication Date: 2013-11-27
FUDAN UNIV
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Problems solved by technology

However, for some hyperspectral images containing sand, minerals, vegetation, and waters, due to the existence of nonlinear mixing phenomena, LMM will get inaccurate results.

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  • Hyperspectral image nonlinear abundance estimation method based on constrained least squares
  • Hyperspectral image nonlinear abundance estimation method based on constrained least squares
  • Hyperspectral image nonlinear abundance estimation method based on constrained least squares

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Embodiment approach

[0091] The nonlinear abundance estimation method (algorithm) based on constrained least squares adopted in the present invention is represented by CNLS-AE.

[0092] 1. Simulation data experiment

[0093] In this section, we test the performance of the proposed algorithm on artificially generated simulation data. We compare the algorithm proposed in this paper with two gradient methods based on GBM and PPNMM, namely GBM-GDA and PPNMM-GDA algorithms mentioned in [7] and [8], respectively. In addition, we also compare with the better performance of the LMM-based FCLS algorithm [10].

[0094] We use Root Mean Square Error (RMSE) and Reconstruction Error (RE) to measure the pros and cons of abundance estimation algorithms. RMSE is used to measure how close the estimated result of the abundance matrix is ​​to the true value. Suppose the endmember abundance obtained by the algorithm is The real abundance is S={s 1 ,s 2 ,·,s N}∈· p×N , then the RMSE is defined as [11]

[0095]...

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Abstract

The invention belongs to the technical field of remote sensing image processing and particularly relates to a hyperspectral image nonlinear abundance estimation method based on constrained least squares. According to the method, through introducing abundance nonnegative constraint, abundance sum-to-one constrain and the bounded constraint of a nonlinear parameter, a hyperspectral image nonlinear unmixing problem is transformed into a problem of solving the constraint nonlinear least squares of an abundance vector and the nonlinear parameter. Furthermore, an alternating iteration optimization algorithm is employed to solve the problem. According to the method, starting from a mixed model of a hyperspectral observation pixel, combined with abundance and nonlinear physics significances in the model, the shortcomings of linear unmixing are effectively overcome, and the method also has good anti noise performance and can be used as an effective solution of the hyperspectral remote sensing image nonlinear unmixing. The method has an important application value at the aspect of high precision unmixing based on a hyperspectral remote sensing image and the detection and identification of a ground target.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a nonlinear abundance estimation method for hyperspectral remote sensing images. Background technique [0002] Remote sensing technology is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is one of the most powerful technical means for studying the earth's resources and environment. Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology with spectral technology. The hyperspectral imager simultaneously detects the two-dimensional geometric space and one-dimensional spectral information of the target on dozens to hundreds of very narrow and continuous spectral segments of the electromagnetic spectrum. In the hyperspectral image, each observation pixel can extract a com...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 普晗晔王斌
Owner FUDAN UNIV
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