Hyperspectral image classification method based on singular value decomposition and spatial-spectral domain attention mechanism

Abstract

The invention discloses a hyperspectral image classification method based on singular value decomposition and a spatial-spectral domain attention mechanism. The method comprises steps of reading a hyperspectral image from the data set, wherein the hyperspectral image data set comprises three widely used hyperspectral image data sets, that is, an Indian Pines data set, a Pavia University data set and a Salinas Valley data set; and selecting any one of the data sets and correspondingly processing a class label ground true graph which only has a partial region; performing rough processing on thesample, and constructing an unsupervised feature extraction model based on a singular value decomposition convolutional network; selecting a training set, a verification set and a test set according to a ratio of 10%: 10%: 80% of the training set to the verification set to the test set; carrying out fine processing on the sample, and constructing a double-branch classification model based on a spatial-spectral domain attention mechanism network; training the classification model by using the training data set to obtain a trained classification model; and classifying the test data set by usingthe trained classification model to obtain the category of each pixel point in the test data set. According to the invention, the precision and speed of hyperspectral image classification are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hyperspectral image classification method based on a singular value decomposition and a spatial spectral domain attention mechanism. Background technique [0002] Hyperspectral remote sensing technology for earth observation is widely used in many different fields, such as mining, astronomy, chemical imaging, agriculture, environmental science, wildland fire tracking, and biological threat detection. Hyperspectral image classification technology is an important content of hyperspectral remote sensing earth observation technology, and its specific task is to classify the target represented by each pixel in the hyperspectral image. However, due to the high-dimensional characteristics of hyperspectral images, the similarity between spectra and the lack of training samples, hyperspectral image classification technology faces a series of challenges. [0003] Th...

AI Technical Summary

Problems solved by technology

However, due to the high-dimensional characteristics of hyperspectral images, the similarity between spectra and the lack of training samples, hyperspectral image classification technology faces a series of challenges.

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