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End member learning based hyperspectral image sparse unmixing method

A technology of hyperspectral image and endmember learning, which is applied in the field of image processing and image unmixing, and can solve the problems of low efficiency of hyperspectral image unmixing.

Active Publication Date: 2016-02-10
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a hyperspectral image sparse unmixing method based on endmember learning to improve the unmixing accuracy of hyperspectral images and overcome the problem of low efficiency of hyperspectral image unmixing. Reduce time-consuming hyperspectral image unmixing

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Embodiment Construction

[0057] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0058] Refer to attached figure 1 , the steps realized by the present invention are further described in detail.

[0059] Step 1, input hyperspectral data.

[0060] Step 2, synthesize hyperspectral base data:

[0061] Select all endmembers contained in the hyperspectral data from the digital spectral library to obtain candidate endmembers.

[0062] Initialize the preset area with an alternative end member to obtain an alternative area.

[0063] Wherein, the specific steps of initializing the preset area with the alternative end member are as follows:

[0064] Step 1, input the alternative endmembers.

[0065] Step 2, set an iteration number n, and the initial value of n is set to 1.

[0066] Step 3, preset an image area with a size of 64×64.

[0067] In step 4, the preset image area is equally divided into eight 8×8 areas to obtain segmented areas.

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Abstract

The invention discloses an end member learning based hyperspectral image sparse unmixing method, which mainly solves the problems of low hyperspectral image unmixing precision, poor reconstruction effect, long consumed time and low efficiency during a low-signal-noise-ratio hyperspectral image unmixing process in the prior art. The method comprises the steps of: inputting hyperspectral data; synthesizing hyperspectral base data; performing end member learning; solving a hyperspectral data abundance matrix; calculating a reconstruction error of the hyperspectral data abundance matrix; and outputting an unmixing result. The method adopts a new solving mode, introduces an end member learning thought, has the advantages of high unmixing precision, good reconstruction effect and high efficiency, is simple in solving step and explicit in principle, and can be used for understanding interpretation of hyperspectral images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral image unmixing method based on endmember learning in the technical field of image unmixing. The present invention realizes fast understanding and interpretation of the hyperspectral image by performing simulated learning on the endmembers first, and then using the learned endmembers to solve the abundance. The invention can be used for unmixing processing of hyperspectral images of various digital devices, and can effectively improve the precision of hyperspectral image unmixing. Background technique [0002] Hyperspectral images are composed of hundreds of very narrow bands, which not only contain information in the spectral domain, but also contain rich spatial information. However, the insufficient spatial resolution of the sensor makes it difficult for the pixels in the hyperspectral image to be pure pixels, but mixed pixels composed of a varie...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2323G06F18/23213
Inventor 孟红云张小华童文杰田小林陈佳伟钟桦
Owner XIDIAN UNIV
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