A Survey on Deep Learning for Few-Shot PolSAR Image Classification
Few-shot classification of polarimetric synthetic aperture radar (PolSAR) images is a challenging task due to the scarcity of labeled data and the complex scattering properties of PolSAR data. Traditional deep learning models often suffer from overfitting and catastrophic forgetting in such settings...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4632 |
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| author | Ningwei Wang Weiqiang Jin Haixia Bi Chen Xu Jinghuai Gao |
| author_facet | Ningwei Wang Weiqiang Jin Haixia Bi Chen Xu Jinghuai Gao |
| author_sort | Ningwei Wang |
| collection | DOAJ |
| description | Few-shot classification of polarimetric synthetic aperture radar (PolSAR) images is a challenging task due to the scarcity of labeled data and the complex scattering properties of PolSAR data. Traditional deep learning models often suffer from overfitting and catastrophic forgetting in such settings. Recent advancements have explored innovative approaches, including data augmentation, transfer learning, meta-learning, and multimodal fusion, to address these limitations. Data augmentation methods enhance the diversity of training samples, with advanced techniques like generative adversarial networks (GANs) generating realistic synthetic data that reflect PolSAR’s polarimetric characteristics. Transfer learning leverages pre-trained models and domain adaptation techniques to improve classification across diverse conditions with minimal labeled samples. Meta-learning enhances model adaptability by learning generalizable representations from limited data. Multimodal methods integrate complementary data sources, such as optical imagery, to enrich feature representation. This survey provides a comprehensive review of these strategies, focusing on their advantages, limitations, and potential applications in PolSAR classification. We also identify key trends, such as the increasing role of hybrid models combining multiple paradigms and the growing emphasis on explainability and domain-specific customization. By synthesizing SOTA approaches, this survey offers insights into future directions for advancing few-shot PolSAR classification. |
| format | Article |
| id | doaj-art-34c7101dba8f41ff97464e532c2084ea |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-34c7101dba8f41ff97464e532c2084ea2024-12-27T14:50:44ZengMDPI AGRemote Sensing2072-42922024-12-011624463210.3390/rs16244632A Survey on Deep Learning for Few-Shot PolSAR Image ClassificationNingwei Wang0Weiqiang Jin1Haixia Bi2Chen Xu3Jinghuai Gao4School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaFew-shot classification of polarimetric synthetic aperture radar (PolSAR) images is a challenging task due to the scarcity of labeled data and the complex scattering properties of PolSAR data. Traditional deep learning models often suffer from overfitting and catastrophic forgetting in such settings. Recent advancements have explored innovative approaches, including data augmentation, transfer learning, meta-learning, and multimodal fusion, to address these limitations. Data augmentation methods enhance the diversity of training samples, with advanced techniques like generative adversarial networks (GANs) generating realistic synthetic data that reflect PolSAR’s polarimetric characteristics. Transfer learning leverages pre-trained models and domain adaptation techniques to improve classification across diverse conditions with minimal labeled samples. Meta-learning enhances model adaptability by learning generalizable representations from limited data. Multimodal methods integrate complementary data sources, such as optical imagery, to enrich feature representation. This survey provides a comprehensive review of these strategies, focusing on their advantages, limitations, and potential applications in PolSAR classification. We also identify key trends, such as the increasing role of hybrid models combining multiple paradigms and the growing emphasis on explainability and domain-specific customization. By synthesizing SOTA approaches, this survey offers insights into future directions for advancing few-shot PolSAR classification.https://www.mdpi.com/2072-4292/16/24/4632polarimetric SARimage classificationdeep learningfew-shot learningsurvey |
| spellingShingle | Ningwei Wang Weiqiang Jin Haixia Bi Chen Xu Jinghuai Gao A Survey on Deep Learning for Few-Shot PolSAR Image Classification Remote Sensing polarimetric SAR image classification deep learning few-shot learning survey |
| title | A Survey on Deep Learning for Few-Shot PolSAR Image Classification |
| title_full | A Survey on Deep Learning for Few-Shot PolSAR Image Classification |
| title_fullStr | A Survey on Deep Learning for Few-Shot PolSAR Image Classification |
| title_full_unstemmed | A Survey on Deep Learning for Few-Shot PolSAR Image Classification |
| title_short | A Survey on Deep Learning for Few-Shot PolSAR Image Classification |
| title_sort | survey on deep learning for few shot polsar image classification |
| topic | polarimetric SAR image classification deep learning few-shot learning survey |
| url | https://www.mdpi.com/2072-4292/16/24/4632 |
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