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

A classification model and optical remote sensing technology, applied in the field of image processing, can solve the problems of low efficiency in the training process and low accuracy of image classification, and achieve the effects of enhancing interpretability, improving classification accuracy, and reducing time-consuming

Pending Publication Date: 2020-06-26
XIDIAN UNIV
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[0004] The technical problem to be solved by the present invention is to provide an optical remote sensing image classification method based on an interpreta

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

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[0028] The present invention provides an optical remote sensing image classification method based on an interpretable CNN image classification model, which selects training samples and test samples after building an interpretable CNN network; trains the convolutional neural network ResNet model; performs interpretability testing; use The trained model performs the final test on the test set. The invention can quickly reach the required recognition rate, reduces the time consumption of the network training process, improves the accuracy of remote sensing image classification, and improves the interpretability of the neural network model.

[0029] See figure 1 , The present invention is an optical remote sensing image classification method based on an interpretable CNN image classification model, whether 34 should be deleted. In the general convolutional neural network in the optical remote sensing image classification process, the features obtained by down-sampling lose a lot of d...

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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 ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/13G06N3/047G06N3/045G06F18/241G06F18/2415
Inventor 庞焱萌侯彪焦李成马文萍马晶晶杨淑媛
Owner XIDIAN UNIV
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