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A Multichannel SAR Ground Moving Target Detection Algorithm Based on Subdomain Adaptive Residual Network | IEEE Journals & Magazine | IEEE Xplore

A Multichannel SAR Ground Moving Target Detection Algorithm Based on Subdomain Adaptive Residual Network


Abstract:

Deep learning (DL) has succeeded in the field of target detection and has been introduced into the research works of ground-moving target indication (GMTI) for synthetic ...Show More

Abstract:

Deep learning (DL) has succeeded in the field of target detection and has been introduced into the research works of ground-moving target indication (GMTI) for synthetic aperture radar (SAR) recently. Due to the lack of labeled data in SAR/GMTI, simulated data are usually employed to support the training of networks, which has proved to be a feasible way in practice. Although some simulated data are very close to the real radar data, the fact is that distribution differences between them are inevitable and always lead to a performance loss of the network. Motivated by recent advances in transfer learning, this letter proposes a new method for ground-moving target detection of multichannel SAR systems, namely, subdomain adaptive residual network (SARN). It is built on the basis of ResNet18, and subdomain adaptation is introduced. During the network training, multikernel local maximum mean discrepancy (MK-LMMD) is minimized as well as classification error. Experiments on three-channel SAR data show that the proposed method significantly improves the detection performance as compared with CA-CFAR and the DL method.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 4010705
Date of Publication: 13 September 2023

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