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Remote Sens. 2019, 11(8), 906; https://doi.org/10.3390/rs11080906

D-ATR for SAR Images Based on Deep Neural Networks

1
School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Received: 20 March 2019 / Accepted: 27 March 2019 / Published: 13 April 2019
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Abstract

Automatic target recognition (ATR) can obtain important information for target surveillance from Synthetic Aperture Radar (SAR) images. Thus, a direct automatic target recognition (D-ATR) method, based on a deep neural network (DNN), is proposed in this paper. To recognize targets in large-scene SAR images, the traditional methods of SAR ATR are comprised of four major steps: detection, discrimination, feature extraction, and classification. However, the recognition performance is sensitive to each step, as the processing result from each step will affect the following step. Meanwhile, these processes are independent, which means that there is still room for processing speed improvement. The proposed D-ATR method can integrate these steps as a whole system and directly recognize targets in large-scene SAR images, by encapsulating all of the computation in a single deep convolutional neural network (DCNN). Before the DCNN, a fast sliding method is proposed to partition the large image into sub-images, to avoid information loss when resizing the input images, and to avoid the target being divided into several parts. After the DCNN, non-maximum suppression between sub-images (NMSS) is performed on the results of the sub-images, to obtain an accurate result of the large-scene SAR image. Experiments on the MSTAR dataset and large-scene SAR images (with resolution 1478 × 1784) show that the proposed method can obtain a high accuracy and fast processing speed, and out-performs other methods, such as CFAR+SVM, Region-based CNN, and YOLOv2. View Full-Text
Keywords: D-ATR; SAR images; deep neural network; non-maximum suppression D-ATR; SAR images; deep neural network; non-maximum suppression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Cui, Z.; Tang, C.; Cao, Z.; Liu, N. D-ATR for SAR Images Based on Deep Neural Networks. Remote Sens. 2019, 11, 906.

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