An image classification method based on a multi-scale dense convolutional neural network and a spectral attention mechanism

Abstract

The invention relates to an image classification method based on a multi-scale dense convolutional neural network and a spectral attention mechanism. The multi-scale dense convolutional neural networkis constructed by using a dense connection mechanism, and the dense connection mechanism can effectively alleviate the problem of gradient disappearance, enhance feature propagation, encourage feature multiplexing, greatly reduce the number of parameters, and reduce the demand for training samples in the network training process; In addition, the network is combined with a spectrum attention mechanism, and the characteristic utilization of the spectrum direction is more effective. According to the method, autonomous extraction and high-precision classification of hyperspectral image depth features are realized under the condition of small samples. Compared with an existing hyperspectral image classification method based on deep learning, the hyperspectral image classification method has the advantages that the sample demand is lower, and the precision is higher.

Description

technical field [0001] The invention relates to a small-sample hyperspectral image classification method based on a multi-scale dense convolutional neural network and a spectral attention mechanism, which belongs to the field of image processing. Background technique [0002] Hyperspectral remote sensing images have high spectral resolution, multiple imaging bands, and large amounts of information, and are widely used in remote sensing applications. Hyperspectral image classification technology is a very important content in hyperspectral image processing technology. It mainly includes two parts: feature extraction and classification. The classification features extracted from the original hyperspectral image have a great influence on the subsequent classification accuracy: the robustness of classification features Strong robustness can greatly improve the classification accuracy; on the contrary, classification features with poor robustness will significantly reduce the cla...

AI Technical Summary

Problems solved by technology

[0004] However, the existing methods for extracting spatial-spectral features of hyperspectral images using deep models are very complicated. It is often necessary to compress and reduce the dimension of the original hyperspectral image in the spectral space, and then extract the spatial and spectral features of the reduced data. Combined to get spatial spectral features

Dimensionality reduction is computationally intensive, and certain spectral information is lost, affecting accuracy

In addition, the existing methods for extracting spatial spectral features of hyperspectral images using deep models require a large number of training samples to train the network, but it is very difficult to mark the collected hyperspectral images in practice. plenty of time

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