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Hyperspectral image nonlinearity solution blending method based on neural network

A hyperspectral image and neural network technology, which is applied in neural learning methods, biological neural network models, image data processing, etc.

Inactive Publication Date: 2016-09-28
TIANJIN UNIV
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For training neural networks, there is no clear method, which means that the user must train many times to find a good parameter setting

Method used

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  • Hyperspectral image nonlinearity solution blending method based on neural network
  • Hyperspectral image nonlinearity solution blending method based on neural network
  • Hyperspectral image nonlinearity solution blending method based on neural network

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[0081] The present invention selects the hyperspectral image taken by the AVRIS sensor in Moffett, Nevada, USA in 1997 as the test object. Since the original image is too large and the calculation complexity is high, a sub-image block of 50×50 is selected for the experiment. It originally contains 189 bands, the band range is 401-889nm, such as figure 2 shown.

[0082] From the hyperspectral data used, the spectral curves of water (Water), soil (Soil) and vegetation (Vegetation) are obtained, such as image 3 shown. The endmember spectral curve extracted by VCA is basically consistent with the real endmember curve and can be used as the real value.

[0083] The present invention uses real data experiments to verify the performance of the algorithm, and the experiments use reconstruction errors (ReconstructionError, RE) and spectral angular distances (Spectral Angle Mapper, SAM) to evaluate the reconstruction performance of the present invention.

[0084] R ...

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Abstract

The invention belongs to the image processing technology field and provides a hyperspectral image nonlinearity solution blending method based on a neural network. By using the method, corresponding work of extracting an end member and acquiring end member abundance and a nonlinear coefficient can be effectively completed so that a solution blending effect is further increased. The method comprises the following steps of (1) inputting hyperspectral image data; (2) randomly generating a sufficient number of training samples and testing samples; (3) using the trained multilayer perceptron neural network to extract abundance and a nonlinear coefficient of a single pixel point in a hyperspectral image; (4) making the acquired abundance satisfy a corresponding constraint condition; (5) repeatedly carrying out solution blending on all the pixel points in the image and then stopping calculation; otherwise, returning to the step (3); and (6) evaluating the performance of the algorithm by calculating a reconstruction error and a spectral angle distance. The method is mainly used for image processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image unmixing method, in particular to a hyperspectral image unmixing method based on a neural network. Background technique [0002] With the development of science and technology, remote sensing and earth observation technology has become increasingly mature, and has gradually become one of the important means of obtaining spatial geographic information. However, due to the limitation of the spatial resolution of the hyperspectral imager and the complexity of natural objects, some pixels of the obtained remote sensing images may be a mixture of several different material spectra, that is, mixed pixels. How to effectively realize the decomposition of mixed pixels has become an important direction of remote sensing research. The accurate decomposition of mixed pixels has important application value for high-precision ground object classification and groun...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/08G06T3/40
CPCG06N3/08G06T3/4069G06V20/13
Inventor 李锵王旭陈雷张立毅刘静光
Owner TIANJIN UNIV
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