Hyperspectral image classification method based on convolutional network and cyclic neural network

Abstract

The invention discloses a hyperspectral image classification method based on a convolutional network and a cyclic neural network, and mainly solves a problem that the classification precision of a hyperspectral image is low in the prior art. The method comprises the following specific steps: (1) constructing a three-dimensional convolutional neural network; (2) constructing the cyclic neural network; (3) preprocessing a to-be-classified hyperspectral image matrix; (4) generating a training data set and a test data set; (5) training the network through the training data set; (6) extracting thespatial features and spectral features of the test data set; (7) merging the spatial features and spectral features; (8) classifying the test data set. The method introduces the three-dimensional convolutional neural network and the cyclic neural network for extracting the spatial features and spectral features of the hyperspectral image, achieves the classification through the fusion of the two types of features, and is high in classification precision of the hyperspectral image.

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 convolutional network and a recurrent neural network in the technical field of hyperspectral image classification. The present invention can be used to classify ground objects in hyperspectral images and identify ground objects in the fields of resource exploration, forest coverage, disaster monitoring and the like. Background technique [0002] In recent years, more and more attention has been paid to the automatic interpretation of hyperspectral images, which has important value and can be applied to agricultural, geological and military aspects such as change detection and disaster control. Each pixel of the hyperspectral image is obtained by using hundreds of high-resolution continuous electromagnetic spectrum observations, so each pixel contains rich spectral information, and the ability to distinguish dif...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that using a single pixel of the hyperspectral image to construct a feature vector only uses the spectral information of the pixel, ignoring the spatial correlation and similarity between the pixel and its neighboring pixels. The extraction of spatial information and spectral information of hyperspectral images is not comprehensive, and the classification accuracy is not high

The shortcomings of this method are that there are too many network training parameters, a large number of samples are required for training, the training time is long, and the classification speed is slow; moreover, using a network to simultaneously extract two different types of features, spectral features and spatial features, ignores The uniqueness and timing of spectral features are compromised, resulting in insufficient and incomplete features extracted and low classification accuracy.

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