Semi-supervised high-resolution remote sensing image scene classification method based on generative adversarial network

Abstract

The invention provides a semi-supervised high-resolution remote sensing image scene classification method based on a generative adversarial network. The method comprises: constructing an EMGAN model:changing the discriminator of the generative adversarial network from binary classification to multi-classification to obtain an EMGAN discriminator, and adding an information entropy maximization network to the generator of the generative adversarial network to obtain an EMGAN generator; training an EMGAN model; dividing a loss function of the EMGAN discriminator into a supervision part and an unsupervised part according to whether a training image has a label or not; dividing a loss function of the EMGAN generator into a feature matching loss function and a generated image information entropy loss function; alternately training the EMGAN discriminator and the EMGAN generator; finely adjusting the VGGNet-16 model; training an SVM model; and fusing the features of the EMGAN model and the VGGNet-16 model, and performing scene classification to obtain a classification result. The remote sensing image scene classification method can effectively improve the precision of remote sensing image scene classification under the condition of few training samples.

Description

technical field [0001] The invention relates to the technical field of machine learning-based high-resolution remote sensing image scene classification, in particular to a semi-supervised high-resolution remote sensing image scene classification method based on a generative confrontation network. Background technique [0002] Land cover is the ultimate manifestation of the human-land interaction process, and it is also the most obvious landscape symbol of the earth's surface system. Land cover changes will trigger a series of environmental changes. Remote sensing technology has become the most effective means of obtaining land cover information because it can provide dynamic, abundant and cheap data sources. In recent years, with the continuous development of remote sensing imaging technology, it has been possible to obtain aerial or satellite remote sensing images with multiple resolutions (spatial resolution, spectral resolution, radiation resolution and temporal resolutio...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the technical problem that the feature extraction accuracy of the existing high-resolution remote sensing image scene classification method is low and a large number of samples are required, the present invention proposes a semi-supervised high-resolut

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