Multi-spectral image classification method guided by multiple attention mechanisms

The invention discloses a multi-spectral image classification method guided by multiple attention mechanisms, and the method comprises the steps: firstly carrying out the dimension reduction of an original image through principal component analysis (PCA), segmenting the original image into data blocks, enabling an output result to pass through three feature modules, optimizing a three-dimensional convolutional neural network through the attention mechanisms, extracting the spatial spectrum features, inputting the spatial spectrum features into a gated loop (GRU) unit, and carrying out the recognition of the spatial spectrum features. The output size is adjusted through a full-connection neural network, high-layer feature information and low-layer feature information are effectively fused, finally, output data of a feature module and the output data of the recurrent neural network are merged and input into a Softmax layer for classification, and a final classification result is obtained. According to the metho