Hyperspectral image deep learning classification method and device, equipment and storage medium

Abstract

The invention relates to the technical field of hyperspectral image classification, and discloses a hyperspectral image deep learning classification method, device and equipment and a storage medium,which are used for improving the accuracy and efficiency of hyperspectral image classification. The method comprises the following steps: acquiring a to-be-classified hyperspectral image; carrying outrandom clipping on a to-be-classified hyperspectral image according to a preset window size and the marked sample set to obtain a to-be-trained sample set; expanding the data set through image transformation to obtain a corresponding deep learning sample set; extracting spatial spectrum features by adopting a convolutional recurrent neural network and a three-dimensional convolutional neural network; and classifying the hyperspectral images through a preset neural network classification model obtained through training to obtain a corresponding image classification result. By constructing thedeep neural network model, the deep abstract features of the hyperspectral image can be automatically extracted, the workload of manual feature extraction and optimization is effectively reduced, andthe end-to-end automatic identification and classification of the hyperspectral image are realized.

Description

technical field [0001] The present invention relates to the technical field of hyperspectral image classification, in particular to a hyperspectral image deep learning classification method, device, equipment and storage medium. Background technique [0002] Hyperspectral image classification is used to assign a certain category label to each pixel, which is a crucial technical means in the field of image information processing and analysis. The spectral features and spatial features contained in hundreds of continuous spectral segments in hyperspectral images can effectively improve the classification accuracy and classification effect, and become indispensable and important information in the process of hyperspectral image classification. However, due to the high dimensionality of hyperspectral images and few labeled samples available, hyperspectral image classification still faces great challenges. [0003] Therefore, how to efficiently and intelligently extract and util...

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Problems solved by technology

[0005] The main purpose of the present invention is to solve the problems of low image classification accuracy and low efficiency in existing hyperspectral image classification methods

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