Hyperspectral image classification method and system based on 3D CutMix-Transform

Abstract

The invention discloses a hyperspectral image classification method and system based on 3D CutMix-Transform. The method comprises the following steps: dividing hyperspectral data into a labeled training data set and a labeled verification data set; the 3D CutMix is pre-trained through the CNN; training a region-level teacher model and a sample-level teacher model by using enhanced data obtained by performing 3D CutMix on the training data set; and jointly training a student model by using the two teacher models and a small amount of labeled data sets. According to the method, 3D CutMix pre-training is carried out by using CNN, then data enhancement is carried out on an original label data set by using 3D CutMix, and respective self-supervision loss and mutual cross pseudo-supervision loss of two teacher models are optimized, so that the co-trained student models are better in robustness, and the training efficiency is improved. The generalization ability and accuracy of the model under the small sample in the existing hyperspectral image classification technology are enhanced and improved, and the method can be used for hyperspectral image classification.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral remote sensing image classification, and in particular relates to a hyperspectral image classification method and system based on 3D CutMix-Transformer. Background technique [0002] With the development of artificial intelligence technology, hyperspectral image intelligent classification technology has deeply affected all aspects of modern life, and its applications in precision agriculture, military, marine, disaster detection and other fields have become more and more extensive. Traditional remote sensing image analysis uses image space information, and the core of hyperspectral image analysis is spectral analysis. Hyperspectral remote sensing data is a spectral image cube, such as figure 1 As shown, its most important feature is the integration of image space dimension and spectral dimension information. Compared with single-band, it has more one-dimensional spectral information. While...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a hyperspectral image classification method and system based on 3DCutMix-Transformer for the deficiencies in the above-mentioned prior art, using 3D CutMix to enhance the hyperspectral image data to amplify the data set, and at the same time Use the one-dimensional Transformer model to learn the features of hyperspectral images, thereby improving the classification accuracy and solving the problems of few labeled samples and poor model robustness in the existing hyperspectral image classification technology

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