Hyperspectral image classification method based on auto-encoder and 3D deep residual network

Abstract

The invention relates to a hyperspectral image classification method based on an auto-encoder and a 3D deep residual network. Compared with the prior art, the defect that remote sensing hyperspectralimage classification is difficult to carry out is overcome. The method comprises the following steps: obtaining a training sample; preprocessing the hyperspectral remote sensing image data to be trained; building and training a stack auto-encoder neural network model; constructing and training a 3D deep residual network; acquiring a hyperspectral remote sensing image to be classified; carrying outpreprocessing and dimensionality reduction of the hyperspectral remote sensing images to be classified; and obtaining a hyperspectral remote sensing image classification result. According to the method, a stack auto-encoder neural network model is built, dimensionality reduction is carried out on an original hyperspectral remote sensing image, and redundant information is eliminated; a residual error network module is introduced through a designed 3D convolutional neural network to properly increase the depth of the network, a 3D deep residual error network is established, space-spectrum joint information of a hyperspectral remote sensing image is extracted more effectively, and the problems of gradient disappearance and network degradation are avoided.

Description

technical field

[0001] The invention relates to the technical field of hyperspectral image processing, in particular to a hyperspectral image classification method based on an autoencoder and a 3D deep residual network. Background technique

[0002] With the continuous and rapid development of hyperspectral remote sensing technology at home and abroad, it has been widely used in agriculture, environmental science, and ground object observation. Hyperspectral remote sensing image data is a three-dimensional data cube containing rich spectral and spatial information. It has information on hundreds of continuous spectral segments of surface objects, which greatly improves the ability to identify and distinguish various types of ground objects. How to make full use of the high-resolution spatial spectrum information of hyperspectral data and continuously improve the classification accuracy has become the goal that researchers are constantly pursuing. However, hyperspectral imag...

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