Multiscale Complex-Valued Feature Attention Convolutional Neural Network for SAR Automatic Target Recognition Article Swipe
Xiaoqian Zhou
,
Cai Luo
,
Peng Ren
,
Bin Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2023.3342986
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2023.3342986
Synthetic aperture radar (SAR) images often lack sufficient attention to target features, inadequately express target feature information, and neglect phase information in conventional Convolutional Neural Network (CNN) recognition methods. These limitations lead to reduced recognition accuracy and slower processing speeds, critical drawbacks for SAR Automatic Target Recognition (ATR) systems. To overcome these challenges, this paper proposes the multi-scale complex-valued feature attention CNN (MsCvFA-CNN) for SAR ATR. MsCvFA-CNN is a model specifically designed for amplitude and phase information of SAR images. A novel complex-valued attention module (CAM) is proposed in this work to focus on the amplitude and phase characteristics of the target separately. By decoupling the amplitude and phase features, the CAM reduces the training time of the network, while preserving the relevant information. Furthermore, the MsCvFA-CNN employs multiple branches for feature extraction with different kernel sizes, which are then combined with CAM in the fusion stage to improve the network's representation of target features. The proposed MsCvFA-CNN is evaluated on both the complex-valued moving and stationary target acquisition and recognition (MSTAR) dataset, as well as the more challenging dataset for urban interpretation (OpenSARUrban). The results demonstrate that it outperforms traditional networks in terms of recognition accuracy and computational efficiency. Specifically, the use of complex-valued networks results in a 2.23% improvement in recognition accuracy compared to traditional real-valued networks. When CAM is added, the network's accuracy is further improved by 3.21%, and the number of epochs required to achieve the highest accuracy is reduced by nearly half.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2023.3342986
- https://ieeexplore.ieee.org/ielx7/4609443/4609444/10364955.pdf
- OA Status
- gold
- Cited By
- 15
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389890812
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389890812Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jstars.2023.3342986Digital Object Identifier
- Title
-
Multiscale Complex-Valued Feature Attention Convolutional Neural Network for SAR Automatic Target RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-18Full publication date if available
- Authors
-
Xiaoqian Zhou, Cai Luo, Peng Ren, Bin ZhangList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2023.3342986Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/4609443/4609444/10364955.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/10364955.pdfDirect OA link when available
- Concepts
-
Computer science, Automatic target recognition, Artificial intelligence, Convolutional neural network, Synthetic aperture radar, Pattern recognition (psychology), Feature extraction, Feature (linguistics), Deep learning, Computer vision, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 7, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2079299474, https://openalex.org/W2117581466, https://openalex.org/W4289820984, https://openalex.org/W2010853190, https://openalex.org/W2248623186, https://openalex.org/W2911158653, https://openalex.org/W3044440238, https://openalex.org/W1979237284, https://openalex.org/W4285506866, https://openalex.org/W4285253304, https://openalex.org/W3167109618, https://openalex.org/W2788805965, https://openalex.org/W2900828225, https://openalex.org/W2383549127, https://openalex.org/W6684191040, https://openalex.org/W4211212913, https://openalex.org/W3008439211, https://openalex.org/W2801497650, https://openalex.org/W2094926474, https://openalex.org/W3013595087, https://openalex.org/W2124092061, https://openalex.org/W3004492228, https://openalex.org/W2884585870, https://openalex.org/W1662198237, https://openalex.org/W2229484904, https://openalex.org/W2560288685, https://openalex.org/W2754361766, https://openalex.org/W3138462497, https://openalex.org/W4285310237, https://openalex.org/W2965619422, https://openalex.org/W3192142973, https://openalex.org/W3114831946, https://openalex.org/W1544180249, https://openalex.org/W2001905757, https://openalex.org/W1498436455, https://openalex.org/W2092898962, https://openalex.org/W4246193833, https://openalex.org/W3048631361, https://openalex.org/W6742954393, https://openalex.org/W2410591237, https://openalex.org/W4296558678, https://openalex.org/W3000436394, https://openalex.org/W3102692100 |
| referenced_works_count | 43 |
| abstract_inverted_index.A | 80 |
| abstract_inverted_index.a | 68, 208 |
| abstract_inverted_index.By | 103 |
| abstract_inverted_index.To | 49 |
| abstract_inverted_index.as | 173, 175 |
| abstract_inverted_index.by | 229, 244 |
| abstract_inverted_index.in | 21, 88, 143, 192, 207, 211 |
| abstract_inverted_index.is | 67, 86, 158, 221, 226, 242 |
| abstract_inverted_index.it | 188 |
| abstract_inverted_index.of | 77, 99, 116, 152, 194, 203, 234 |
| abstract_inverted_index.on | 93, 160 |
| abstract_inverted_index.to | 9, 32, 91, 147, 215, 237 |
| abstract_inverted_index.CAM | 111, 142, 220 |
| abstract_inverted_index.CNN | 61 |
| abstract_inverted_index.SAR | 43, 64, 78 |
| abstract_inverted_index.The | 155, 184 |
| abstract_inverted_index.and | 17, 36, 74, 96, 107, 165, 169, 197, 231 |
| abstract_inverted_index.are | 138 |
| abstract_inverted_index.for | 42, 63, 72, 130, 180 |
| abstract_inverted_index.the | 56, 94, 100, 105, 110, 113, 117, 121, 125, 144, 149, 162, 176, 201, 223, 232, 239 |
| abstract_inverted_index.use | 202 |
| abstract_inverted_index.ATR. | 65 |
| abstract_inverted_index.When | 219 |
| abstract_inverted_index.both | 161 |
| abstract_inverted_index.lack | 6 |
| abstract_inverted_index.lead | 31 |
| abstract_inverted_index.more | 177 |
| abstract_inverted_index.that | 187 |
| abstract_inverted_index.then | 139 |
| abstract_inverted_index.this | 53, 89 |
| abstract_inverted_index.time | 115 |
| abstract_inverted_index.well | 174 |
| abstract_inverted_index.with | 133, 141 |
| abstract_inverted_index.work | 90 |
| abstract_inverted_index.(ATR) | 47 |
| abstract_inverted_index.(CAM) | 85 |
| abstract_inverted_index.(CNN) | 26 |
| abstract_inverted_index.(SAR) | 3 |
| abstract_inverted_index.These | 29 |
| abstract_inverted_index.focus | 92 |
| abstract_inverted_index.half. | 246 |
| abstract_inverted_index.model | 69 |
| abstract_inverted_index.novel | 81 |
| abstract_inverted_index.often | 5 |
| abstract_inverted_index.paper | 54 |
| abstract_inverted_index.phase | 19, 75, 97, 108 |
| abstract_inverted_index.radar | 2 |
| abstract_inverted_index.stage | 146 |
| abstract_inverted_index.terms | 193 |
| abstract_inverted_index.these | 51 |
| abstract_inverted_index.urban | 181 |
| abstract_inverted_index.which | 137 |
| abstract_inverted_index.while | 119 |
| abstract_inverted_index.Neural | 24 |
| abstract_inverted_index.Target | 45 |
| abstract_inverted_index.added, | 222 |
| abstract_inverted_index.epochs | 235 |
| abstract_inverted_index.fusion | 145 |
| abstract_inverted_index.images | 4 |
| abstract_inverted_index.kernel | 135 |
| abstract_inverted_index.module | 84 |
| abstract_inverted_index.moving | 164 |
| abstract_inverted_index.nearly | 245 |
| abstract_inverted_index.number | 233 |
| abstract_inverted_index.sizes, | 136 |
| abstract_inverted_index.slower | 37 |
| abstract_inverted_index.target | 10, 14, 101, 153, 167 |
| abstract_inverted_index.(MSTAR) | 171 |
| abstract_inverted_index.Network | 25 |
| abstract_inverted_index.achieve | 238 |
| abstract_inverted_index.dataset | 179 |
| abstract_inverted_index.employs | 127 |
| abstract_inverted_index.express | 13 |
| abstract_inverted_index.feature | 15, 59, 131 |
| abstract_inverted_index.further | 227 |
| abstract_inverted_index.highest | 240 |
| abstract_inverted_index.images. | 79 |
| abstract_inverted_index.improve | 148 |
| abstract_inverted_index.neglect | 18 |
| abstract_inverted_index.reduced | 33, 243 |
| abstract_inverted_index.reduces | 112 |
| abstract_inverted_index.results | 185, 206 |
| abstract_inverted_index.speeds, | 39 |
| abstract_inverted_index.accuracy | 35, 196, 213, 225, 241 |
| abstract_inverted_index.aperture | 1 |
| abstract_inverted_index.branches | 129 |
| abstract_inverted_index.combined | 140 |
| abstract_inverted_index.compared | 214 |
| abstract_inverted_index.critical | 40 |
| abstract_inverted_index.dataset, | 172 |
| abstract_inverted_index.designed | 71 |
| abstract_inverted_index.improved | 228 |
| abstract_inverted_index.methods. | 28 |
| abstract_inverted_index.multiple | 128 |
| abstract_inverted_index.network, | 118 |
| abstract_inverted_index.networks | 191, 205 |
| abstract_inverted_index.overcome | 50 |
| abstract_inverted_index.proposed | 87, 156 |
| abstract_inverted_index.proposes | 55 |
| abstract_inverted_index.relevant | 122 |
| abstract_inverted_index.required | 236 |
| abstract_inverted_index.systems. | 48 |
| abstract_inverted_index.training | 114 |
| abstract_inverted_index.Automatic | 44 |
| abstract_inverted_index.Synthetic | 0 |
| abstract_inverted_index.amplitude | 73, 95, 106 |
| abstract_inverted_index.attention | 8, 60, 83 |
| abstract_inverted_index.different | 134 |
| abstract_inverted_index.drawbacks | 41 |
| abstract_inverted_index.evaluated | 159 |
| abstract_inverted_index.features, | 11, 109 |
| abstract_inverted_index.features. | 154 |
| abstract_inverted_index.network's | 150, 224 |
| abstract_inverted_index.networks. | 218 |
| abstract_inverted_index.MsCvFA-CNN | 66, 126, 157 |
| abstract_inverted_index.decoupling | 104 |
| abstract_inverted_index.extraction | 132 |
| abstract_inverted_index.preserving | 120 |
| abstract_inverted_index.processing | 38 |
| abstract_inverted_index.stationary | 166 |
| abstract_inverted_index.sufficient | 7 |
| abstract_inverted_index.Recognition | 46 |
| abstract_inverted_index.acquisition | 168 |
| abstract_inverted_index.challenges, | 52 |
| abstract_inverted_index.challenging | 178 |
| abstract_inverted_index.demonstrate | 186 |
| abstract_inverted_index.efficiency. | 199 |
| abstract_inverted_index.improvement | 210 |
| abstract_inverted_index.information | 20, 76 |
| abstract_inverted_index.limitations | 30 |
| abstract_inverted_index.multi-scale | 57 |
| abstract_inverted_index.outperforms | 189 |
| abstract_inverted_index.real-valued | 217 |
| abstract_inverted_index.recognition | 27, 34, 170, 195, 212 |
| abstract_inverted_index.separately. | 102 |
| abstract_inverted_index.traditional | 190, 216 |
| abstract_inverted_index.(MsCvFA-CNN) | 62 |
| abstract_inverted_index.2.23% | 209 |
| abstract_inverted_index.Furthermore, | 124 |
| abstract_inverted_index.conventional | 22 |
| abstract_inverted_index.inadequately | 12 |
| abstract_inverted_index.information, | 16 |
| abstract_inverted_index.information. | 123 |
| abstract_inverted_index.specifically | 70 |
| abstract_inverted_index.3.21%, | 230 |
| abstract_inverted_index.Convolutional | 23 |
| abstract_inverted_index.Specifically, | 200 |
| abstract_inverted_index.computational | 198 |
| abstract_inverted_index.complex-valued | 58, 82, 163, 204 |
| abstract_inverted_index.interpretation | 182 |
| abstract_inverted_index.representation | 151 |
| abstract_inverted_index.(OpenSARUrban). | 183 |
| abstract_inverted_index.characteristics | 98 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.96990716 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |