Interpretable CNN image classification model-based optical remote sensing image classification method

Abstract

The invention discloses an interpretable CNN image classification model-based optical remote sensing image classification method. An explainable CNN ResNet model composed of six groups of 51 basic convolution layers, three full connection layers and one Softmax layer in total is built; the method comprises the following steps of: performing down-sampling by utilizing ResNet; obtaining a feature map containing context information; and then interpretable modification is performed on the ResNet model to obtain a new interpretable CNN network based on the ResNet model, and the ResNet model extracts features through multiple groups of convolution-pooling layers with residual modules and finally inputs the features to a full connection layer to classify the images. The interpretability of an existing deep learning model can be enhanced, and the performance of the model is further improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an optical remote sensing image classification method based on an interpretable CNN (Convolutional Neural Networks) image classification model, which can be used for classification of ground objects such as planes and ships. Background technique [0002] Foreign top Internet companies such as Google and Microsoft, and domestic companies such as Baidu, Tencent and Alibaba have all increased their investment in the artificial intelligence industry. The practical application of the artificial intelligence industry has a subtle influence on people's way of life. In the research field of artificial intelligence, the research of deep learning is a major focus, which can be applied in various fields of artificial intelligence, such as speech signal processing, computer vision and natural language processing. processing) etc. CNNs (Convolution Nerual Networks) can ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an optical remote sensing image classification method based on an interpretable CNN image classification model to solve the problems of low training process efficiency and low image classification accuracy in the prior art. question

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