SAR Target Recognition via Meta-Learning and Amortized Variational Inference
The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/20/5966 |
| _version_ | 1797550242498871296 |
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| author | Ke Wang Gong Zhang |
| author_facet | Ke Wang Gong Zhang |
| author_sort | Ke Wang |
| collection | DOAJ |
| description | The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data. |
| first_indexed | 2024-03-10T15:26:37Z |
| format | Article |
| id | doaj.art-7326f7c396a2441a836c77d15436f2a6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:26:37Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7326f7c396a2441a836c77d15436f2a62023-11-20T18:01:54ZengMDPI AGSensors1424-82202020-10-012020596610.3390/s20205966SAR Target Recognition via Meta-Learning and Amortized Variational InferenceKe Wang0Gong Zhang1School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaThe challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.https://www.mdpi.com/1424-8220/20/20/5966automatic target recognitionmeta-learningamortized variational inference |
| spellingShingle | Ke Wang Gong Zhang SAR Target Recognition via Meta-Learning and Amortized Variational Inference Sensors automatic target recognition meta-learning amortized variational inference |
| title | SAR Target Recognition via Meta-Learning and Amortized Variational Inference |
| title_full | SAR Target Recognition via Meta-Learning and Amortized Variational Inference |
| title_fullStr | SAR Target Recognition via Meta-Learning and Amortized Variational Inference |
| title_full_unstemmed | SAR Target Recognition via Meta-Learning and Amortized Variational Inference |
| title_short | SAR Target Recognition via Meta-Learning and Amortized Variational Inference |
| title_sort | sar target recognition via meta learning and amortized variational inference |
| topic | automatic target recognition meta-learning amortized variational inference |
| url | https://www.mdpi.com/1424-8220/20/20/5966 |
| work_keys_str_mv | AT kewang sartargetrecognitionviametalearningandamortizedvariationalinference AT gongzhang sartargetrecognitionviametalearningandamortizedvariationalinference |