Hyperspectral image classification method based on fusion of multi-scale and multi-dimensional spatial-spectral characteristics

Abstract

The invention discloses a hyperspectral image classification method based on fusion of multi-scale and multi-dimensional spatial spectral characteristics. The method comprises the following steps: (1)inputting a hyperspectral image; (2) preprocessing the hyperspectral images to be classified; (3) tracking neighborhood blocks; (4) generating a training set and a test set; (5) constructing a multi-scale spatial spectrum feature and multi-dimensional feature fusion network; (6) training a multi-scale spatial spectrum feature and multi-dimensional feature fusion network; and (7) classifying the test samples. The method provided by the invention can effectively solve the problems of too single feature and too single scale of the convolutional neural network during training, can solve the problem of low average classification precision AA during hyperspectral classification, can maintain the recognition capability of small sample number categories while realizing high classification precision, and is good in classification performance.

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 fusion of multi-scale and multi-dimensional spatial spectral features in the technical field of image classification. The present invention can be applied to many fields such as disaster monitoring, geological exploration, urban planning, target recognition, etc. by analyzing the types of ground objects in hyperspectral images. Background technique [0002] Hyperspectral records the continuous spectral characteristics of ground objects with its rich band information, and has the possibility of recognizing more types of ground objects and classifying objects with higher accuracy. The key to hyperspectral image classification technology is to use a small number of training samples to obtain higher classification accuracy. Recently, with the wide application of deep learning in various fields, hyperspectral classif...

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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 classification method based on fusion of multi-scale and multi-dimensional spatial-spectral features, which fuses related information between features of different scales, and realizes high-dimensional features and low-dimensional features at the same time , The fusion of spatial features and spectral features is used to solve the problem of low classification accuracy in the existing hyperspectral image classification methods, and the poor classification effect of ground object categories with non-concentrated sample distribution or small sample size

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