Hyperspectral image classification method and system based on cascaded spatial-spectral feature fusion and kernel extreme learning machine

Abstract

The invention discloses a hyperspectral image classification method and system based on cascade spatial-spectral feature fusion and a kernel extreme learning machine, and the method comprises the steps: carrying out the normalization preprocessing operation of a hyperspectral image, and dividing a data set into a training set and a test set; performing convolution on an input hyperspectral image by using a space-spectrum attention residual neural network to respectively obtain space and spectrum information; performing space-spectrum feature extraction on data of a test set by using the trained network, calculating an output weight matrix of a hidden layer of a kernel extreme learning machine while training the neural network, and then inputting the extracted features and the output weight matrix into the kernel extreme learning machine, thereby achieving the purpose of classifying hyperspectral images. According to the method, spectral attention information and spatial attention information of the hyperspectral image are fully utilized, and deep feature extraction can be performed on the hyperspectral remote sensing data, so that rapid and accurate classification is realized.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing, and in particular relates to a hyperspectral image classification method and system of cascaded space-spectrum feature fusion and nuclear extreme learning machine. Background technique [0002] In recent years, the development of space information technology and the increase in the number of satellites have provided a wealth of remote sensing image data with spatial and spectral information for the development of remote sensing technology. As a special remote sensing image, hyperspectral image has a wide spectral coverage, including ultraviolet, visible light, near-infrared and mid-infrared regions, and contains rich spatial and spectral information. It is widely used in environmental monitoring and urban planning. , national defense, geological survey and crop detection and other fields. [0003] Hyperspectral image processing includes classification, unmixing, change detection, a...

AI Technical Summary

Problems solved by technology

In many application fields of hyperspectral technology, the classification of ground object information based on hyperspectral images is an important link. Although the data processing of hyperspectral images has made great achievements, the data processing technology is far from meeting the needs of reality.

At present, the two main problems encountered in hyperspectral image classification are: (1) band redundancy under the condition of limited samples, the Hughes phenomenon caused by the contradiction between the high dimension of hyperspectral data and limited training samples is the main reason for its application in classification A key problem we are facing; (2) Insufficient utilization of spatial information. Hyperspectral images are not just a disordered collection of pixels. The direct reflection of orderly arrangement is spatial characteristics. Improper extraction and analysis of spatial information affects the classification effect, which is some of the challenges we face

However, as the number of network layers increases, the CNN model is prone to gradient disappearance during training.

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