Abstract
Abstract
Aiming at the problem that the convolutional neural network is difficult to recognize the posture transformation of the image, which requires a large number of label samples for training. This paper proposes an improved synthetic aperture radar image classification method based on the combination of dilated convolution and capsule network. We use dilated convolution to expand the receptive field while reducing the amount of calculation, and use the capsule network to improve the classification effect when there are fewer training samples. The experimental results show that the proposed method can still achieve high classification performance after the training sample is small and the test sample undergoes pose transformation, and at the same time, it has a low computational load.
Subject
General Physics and Astronomy
Reference8 articles.
1. Target classification using the deep convolutional networks for SAR images[J];Chen;IEEE Transactions on Geoscience and Remote Sensing,2016
2. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J];Zhang;IEEE Transactions on Geoscience and Remote Sensing,2017
3. Deep learning for target classification from SAR imagery: Data augmentation and translation invariance[J];Furukawa;IEICE Technical Report,2017
4. A coupled convolutional neural network for small and densely clustered ship detection in SAR images[J];Juanping;Science China Information Sciences,2019
5. Convolutional Neural Network With Data Augmentation for SAR Target Recognition[J];Ding,2016
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