Hyperspectral Remote Sensing Image Classification Method Based on Attention Mechanism and Convolutional Neural Network

Abstract

The invention provides a hyperspectral remote sensing image classification method based on the attention mechanism and convolutional neural network, which performs principal component analysis on the original hyperspectral remote sensing image to reduce the dimensionality, and samples and blocks the hyperspectral data after dimensionality reduction; then performs 3D Convolution operation and pooling operation to obtain the intermediate feature map; then each spectral vector of the intermediate feature is multiplied by the spectral attention module and each spatial feature and the spatial attention module respectively to obtain the attention enhancement sample; Then perform another convolution operation and attention enhancement operation; then input the intermediate feature map obtained through the 3D convolution operation into the classifier for classification. The invention has the beneficial effects of reducing the classification cost, improving the classification performance, realizing adaptive feature refinement through the extraction and enhancement of sample features, and further improving the classification accuracy of hyperspectral remote sensing images.

Description

technical field [0001] The invention relates to the field of hyperspectral image classification, in particular to a hyperspectral remote sensing image classification method based on an attention mechanism and a convolutional neural network. Background technique [0002] Remote sensing is a long-distance, non-contact target detection technology and method, and it is an important means for people to study the characteristics of ground objects. With the rapid development of hardware technology and the continuous growth of application requirements, the obtained remote sensing images have gradually developed from wide-band imaging to narrow-band imaging, and at the same time present the characteristics of high spatial resolution, high spectral resolution, and high temporal resolution. Remote sensing was born from this. Hyperspectral remote sensing technology is a very iconic achievement in the history of remote sensing development. Its rapid development has attracted extensive a...

AI Technical Summary

Problems solved by technology

In the deep learning method, the deep neural network is the representative. The convolutional neural network in the deep neural network has achieved good applications in the classification of hyperspectral remote sensing images. However, the amount of input information of the convolutional neural network is not completely positively correlated with the classification effect. , under a certain model, too complex input will not only increase the training time and classification time, but even cause the accuracy to decrease instead of increase.

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