High-spectrum image nonlinear demixing method based on neural network and differential search

Abstract

The invention belongs to the technical filed of image processing, and provides a novel high-spectrum image nonlinear demixing method, for realizing the purpose of completing corresponding demixing work of a high-spectrum image with remarkable high-order nonlinear mapping and multilayer scattering between end members. To this end, the technical scheme adopted by the invention brings forward a high-spectrum image nonlinear demixing method based on a neural network and differential search. The method comprises the following steps: 1, inputting high-spectrum image data; 2, according to a p-order polynomial model, randomly generating a training sample for training a multilayer perceptron neural network (MLP); 3, by use of the trained MLP, extracting a nonlinear order of a single pixel point of an image; 4, determining dimensions and position coding of a search individual through a high-spectrum image end member number R; 5, calculating a fitness value of each individual; 6, obtaining a global optimal position of a whole organism through search; 7, completing demixing of the individual pixel point, and otherwise, continuously performing optimization; and 8, stopping calculation after the demixing, and otherwise, continuously demixing a next pixel. The method provided by the invention is mainly applied to 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 and a differential search algorithm (DSA). 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. The key to distinguish hyperspectral remote sensors from traditional multispectral sensors is narrow-band imaging. The spectral resolution in the visible-near-infrared region reaches the nanometer level, and detailed and accurate spectral information of the research object can be obtained, so it has a wide range of applications. [0003] Due to the limited spatial resolution of hyperspectral imaging spectrometers and the complexity and diversity of natural object...

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Problems solved by technology

[0008] Some scholars have proposed a gradient algorithm based on different bilinear models, but it has the disadvantage of being easily affected by the initial value and falling into local convergence and only applicable to a single model.

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