Remote sensing image classification method based on attention mechanism deep Contourlet network

Abstract

The invention discloses a remote sensing image classification method based on an attention mechanism deep Contourlet network, and the method comprises the steps: building a remote sensing image library, and obtaining a training sample set and a test sample set; then, setting a Contourlet decomposition module, building a convolutional neural network model, grouping convolution layers in the model in pairs to form a convolution module, using an attention mechanism, and performing data enhancement on the merged feature map through a channel attention module; carrying out iterative training; performing global contrast normalization processing on the remote sensing images to be classified to obtain the average intensity of the whole remote sensing images, and then performing normalization to obtain the remote sensing images to be classified after normalization processing; and inputting the normalized unknown remote sensing image into the trained convolutional neural network model, and classifying the unknown remote sensing image to obtain a network output classification result. According to the method, a Contourlet decomposition method and a deep convolutional network method are combined, a channel attention mechanism is introduced, and the advantages of deep learning and Contourlet transformation can be brought into play at the same time.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing image classification method based on an attention mechanism deep Contourlet network. Background technique [0002] Remote sensing image scene classification is the basic task of remote sensing image understanding, which can be applied to military and civilian fields. With the development of remote sensing technology, the quality of the captured remote sensing images has also been improved. The features contained in the images are more detailed and the spectral features are more complex. This has led to many early remote sensing image recognition methods in the current high-quality The accuracy of the image classification task is not as good as expected. [0003] In recent years, deep learning theory has developed rapidly in the field of image processing, and its performance has been superior to traditional image classification algorithms. Ma...

AI Technical Summary

Problems solved by technology

However, the deep learning model currently applied to remote sensing image classification has many limitations, ignoring the spectral information of the image, but directly allowing the model to fit parameters from the input pixel-level image, and the scattered search space also brings a large number of unknown parameters. and training speed limitations

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