Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN
Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic t...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/9141023 |
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| _version_ | 1832551695023865856 |
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| author | Mengmeng Huang Fang Liu Xianfa Meng |
| author_facet | Mengmeng Huang Fang Liu Xianfa Meng |
| author_sort | Mengmeng Huang |
| collection | DOAJ |
| description | Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set. |
| format | Article |
| id | doaj-art-abb9bf7c05834d6bb18eff8e4f1eb1e0 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-abb9bf7c05834d6bb18eff8e4f1eb1e02025-02-03T06:00:49ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/9141023Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNNMengmeng Huang0Fang Liu1Xianfa Meng2Automatic Target Recognition Key LabAutomatic Target Recognition Key LabAutomatic Target Recognition Key LabSynthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set.http://dx.doi.org/10.1155/2021/9141023 |
| spellingShingle | Mengmeng Huang Fang Liu Xianfa Meng Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN Journal of Mathematics |
| title | Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN |
| title_full | Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN |
| title_fullStr | Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN |
| title_full_unstemmed | Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN |
| title_short | Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN |
| title_sort | few samples of sar automatic target recognition based on enhanced shape cnn |
| url | http://dx.doi.org/10.1155/2021/9141023 |
| work_keys_str_mv | AT mengmenghuang fewsamplesofsarautomatictargetrecognitionbasedonenhancedshapecnn AT fangliu fewsamplesofsarautomatictargetrecognitionbasedonenhancedshapecnn AT xianfameng fewsamplesofsarautomatictargetrecognitionbasedonenhancedshapecnn |