Hyperspectral classification method of lightweight hybrid convolution model based on global reasoning

Abstract

The invention discloses a hyperspectral classification method of a lightweight hybrid convolution model based on global reasoning, belongs to the technical field of information processing, and is mainly used for hyperspectral image classification of small sample data. The lightweight hybrid convolution model based on global reasoning comprises a layer of two-dimensional convolution, a layer of three-dimensional convolution and a global reasoning module; a global reasoning module is added, global feature information and deep feature information of the hyperspectral image are effectively extracted by reasoning context relationships among different regions, feature extraction of deep three-dimensional convolution is replaced, and the complexity and calculation cost of the model are greatly reduced. A test result in a public data set shows that the classification performance of the method is superior to that of the current best classification method, the space-spectrum joint features of the hyperspectral image can be effectively extracted only by a small number of training samples, and the method is high in practicability. The problem that channel relation information is lost due to the fact that only two-dimensional convolution is used, and the problem that model complexity and calculation cost are greatly increased due to the fact that deep three-dimensional convolution is adopted are solved.

Description

technical field [0001] The invention discloses a hyperspectral classification method based on a lightweight hybrid convolution model based on global reasoning, and belongs to the technical field of information processing. Background technique [0002] Hyperspectral imagery (HSI) has brought great help to the extraction of ground object information because of its many spectral bands, but a large amount of spectral data also causes redundancy of ground object information, and at the same time, due to the improvement of spectral resolution, each The correlation between bands is strengthened, which brings great challenges to the classification of hyperspectral images. Especially when there are fewer training samples, it will bring greater difficulties to the classification of HSI. [0003] At present, there are two main types of HSI classification methods: traditional methods based on spectral information and methods based on deep learning. Traditional methods mainly use the s...

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Problems solved by technology

[0006] The purpose of the present invention is to provide a hyperspectral classification method based on a lightweight hybrid convolution model based on global reasoning, to solve the problem in the prior art that the hyperspectral classification method includes multiple three-dimensional convolutional layers, resulting in complex models, high calculation costs, and sample big demand problem

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