Learning From Natural Images in Few-Shot SAR Target Classification
The intricate imaging attributes of synthetic aperture radar (SAR) present a formidable challenge to the prevailing few-shot target classification. In order to address this issue, we study how to leverage natural images to assist with few-shot SAR learning and propose a model with cross-domain gener...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10946144/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849316063801507840 |
|---|---|
| author | Songhao Shi Xiaodan Wang Yafei Song |
| author_facet | Songhao Shi Xiaodan Wang Yafei Song |
| author_sort | Songhao Shi |
| collection | DOAJ |
| description | The intricate imaging attributes of synthetic aperture radar (SAR) present a formidable challenge to the prevailing few-shot target classification. In order to address this issue, we study how to leverage natural images to assist with few-shot SAR learning and propose a model with cross-domain generalization ability, named CDFS-SAR. The core of our approach is a dual-branch network based on supervised contrastive learning. This network comprises a global branch for holistic image understanding and a local branch for capturing fine-grained image details. By exploiting the potential semantic consistency between these branches, we facilitate the learning of background-separated prior knowledge, enabling effective cross-domain generalization to SAR images. To further address the challenge of category confusion in SAR images, we introduce an embedding space reconstruction module. This module utilizes supervised contrastive learning to enhance intraclass compactness and interclass divergence of features. In addition, we employ a Brownian distance covariance metrics module to overcome the limitations of conventional metric distances, ensuring the model acquires more precise semantic representations. A novel joint loss function is formulated to optimize cross-domain generalization and accommodate the unique imaging characteristics of SAR. Experimental results on the MSTAR and OpenSARShip datasets demonstrate the effectiveness of our method and highlight the ability to improve few-shot SAR target classification. |
| format | Article |
| id | doaj-art-95bc6b7f7ed148e38738456133d92bb2 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-95bc6b7f7ed148e38738456133d92bb22025-08-20T03:51:58ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118105951060710.1109/JSTARS.2025.355659710946144Learning From Natural Images in Few-Shot SAR Target ClassificationSonghao Shi0https://orcid.org/0009-0008-0232-8460Xiaodan Wang1https://orcid.org/0000-0003-2785-9539Yafei Song2https://orcid.org/0000-0003-0962-0671Air Defense and Antimissile School, Air Force Engineering University, Xi'an, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi'an, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi'an, ChinaThe intricate imaging attributes of synthetic aperture radar (SAR) present a formidable challenge to the prevailing few-shot target classification. In order to address this issue, we study how to leverage natural images to assist with few-shot SAR learning and propose a model with cross-domain generalization ability, named CDFS-SAR. The core of our approach is a dual-branch network based on supervised contrastive learning. This network comprises a global branch for holistic image understanding and a local branch for capturing fine-grained image details. By exploiting the potential semantic consistency between these branches, we facilitate the learning of background-separated prior knowledge, enabling effective cross-domain generalization to SAR images. To further address the challenge of category confusion in SAR images, we introduce an embedding space reconstruction module. This module utilizes supervised contrastive learning to enhance intraclass compactness and interclass divergence of features. In addition, we employ a Brownian distance covariance metrics module to overcome the limitations of conventional metric distances, ensuring the model acquires more precise semantic representations. A novel joint loss function is formulated to optimize cross-domain generalization and accommodate the unique imaging characteristics of SAR. Experimental results on the MSTAR and OpenSARShip datasets demonstrate the effectiveness of our method and highlight the ability to improve few-shot SAR target classification.https://ieeexplore.ieee.org/document/10946144/Contrastive learningcross-domain few-shot learning (CDFSL)prototypical networksynthetic aperture radar (SAR) target classification |
| spellingShingle | Songhao Shi Xiaodan Wang Yafei Song Learning From Natural Images in Few-Shot SAR Target Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Contrastive learning cross-domain few-shot learning (CDFSL) prototypical network synthetic aperture radar (SAR) target classification |
| title | Learning From Natural Images in Few-Shot SAR Target Classification |
| title_full | Learning From Natural Images in Few-Shot SAR Target Classification |
| title_fullStr | Learning From Natural Images in Few-Shot SAR Target Classification |
| title_full_unstemmed | Learning From Natural Images in Few-Shot SAR Target Classification |
| title_short | Learning From Natural Images in Few-Shot SAR Target Classification |
| title_sort | learning from natural images in few shot sar target classification |
| topic | Contrastive learning cross-domain few-shot learning (CDFSL) prototypical network synthetic aperture radar (SAR) target classification |
| url | https://ieeexplore.ieee.org/document/10946144/ |
| work_keys_str_mv | AT songhaoshi learningfromnaturalimagesinfewshotsartargetclassification AT xiaodanwang learningfromnaturalimagesinfewshotsartargetclassification AT yafeisong learningfromnaturalimagesinfewshotsartargetclassification |