Open AccessSAR image classification method based on improved capsule network
Author(s) -
Zezhou Yang,
Shaodi Jing
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012181
Subject(s) - computer science , artificial intelligence , convolution (computer science) , convolutional neural network , transformation (genetics) , pattern recognition (psychology) , sample (material) , image (mathematics) , artificial neural network , capsule , field (mathematics) , computer vision , mathematics , biochemistry , chemistry , botany , chromatography , biology , pure mathematics , gene
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.