Hyperspectral image classification method based on combined multi-level spatial spectrum information CNN

Abstract

The invention provides a hyperspectral image classification method based on combined multi-level spatial spectrum information CNN, and mainly solves the problems of poor classification performance andpoor classification area consistency of hyper-spectral images. The method comprises the following implementation steps: inputting a hyperspectral data set; constructing a convolutional neural networkand a multi-stage spatial spectrum information extraction network; generating a combined multi-level spatial spectrum information convolutional neural network CNN; inputting a training sample set, and training the network by using a loss function; and inputting the test data set, and classifying the hyperspectral image by using the trained combined multi-level spatial spectrum information convolutional neural network CNN. According to the invention, the built combined multi-stage spatial spectrum information convolutional neural network CNN is used; according to the method, the multi-level spatial information and the global inter-spectral information of the hyperspectral image can be extracted and fused, the problems that in the prior art, spatial feature information is not fully utilized, a convolution kernel cannot extract the spectral global information, and consequently the consistency of classification areas is poor and the precision is not high are solved, and the classificationaccuracy of the hyperspectral image is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral image classification method based on a multi-level spatial spectral information joint convolutional neural network CNN (convolutional neural network) in the technical field of image classification. The present invention can be applied to fields such as geological exploration and land utilization by analyzing the types of ground objects in hyperspectral images, and provides necessary information support for geological research. Background technique [0002] With the development of remote sensing technology and imaging technology, the application fields of hyperspectral remote sensing technology are becoming more and more extensive. Hyperspectral images can obtain approximately continuous spectral information of target objects in a large number of bands such as ultraviolet, visible light, near-infrared, and mid-infrared, and describe the spatial distr...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the shortcomings of the above-mentioned prior art, and propose a hyperspectral image classification method based on joint multi-level spatial spectral information CNN, which is used to solve the problems of low classification accuracy and regional Technical issues with poor consistency

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