Hyperspectral image classification method based on singular value decomposition and spatial-spectral domain attention mechanism
A singular value decomposition and hyperspectral image technology, which is applied in the field of hyperspectral image classification based on singular value decomposition and spatial spectral domain attention mechanism, can solve the problems of similarity between spectra and few training samples, so as to improve accuracy and Classification speed, ease of extraction, improved use of spectrum
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[0070] The present invention provides a hyperspectral image classification method based on singular value decomposition and space spectral domain attention mechanism, which reads hyperspectral image data from a data set; randomly selects some sample blocks and inputs them into a singular value decomposition convolutional network to obtain features, which is The first step is rough processing and unsupervised extraction of features; proportional selection of training set, test set and verification set; construction of a dual-branch classification model based on the spatial spectral domain attention mechanism network; training the classification model with the training data set; A good classification model classifies the test dataset. The present invention adopts the "coarse to fine" processing operation, combines the characteristics of hyperspectral data with rich spectral information and spatial information, uses the attention mechanism to pay attention to important information an
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