Recognition in Label and Discrimination in Feature: A Hierarchically Designed Lightweight Method for Limited Data in SAR ATR

Wang, Chenwei; Pei, Jifang; Yang, Jianyu; Liu, Xiaoyu; Huang, Yulin; Mao, Deqing

doi:10.1109/tgrs.2022.3225187

Cited by 10 publications

(2 citation statements)

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“…Cai et al introduced spatial transformation to enhance the prototype network, extracting semantic information from SAR images and enhancing SAR ATR performance under limited sample conditions, as presented in [37]. Wang et al proposed a novel hierarchically designed lightweight method (HDLM) that addresses the issue of limited data in SAR ATR through label recognition and feature discrimination, as demonstrated in [38]. These methods demonstrate the adaptation and application of successful techniques in natural image recognition to address the challenges of small sample SAR image recognition.…”

Section: A Small Sample Sar Atrmentioning

confidence: 99%

LDCL: Low-Confidence Discriminant Contrastive Learning for Small-Sample SAR ATR

Liao,

Zhai,

Wang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Synthetic Aperture Radar (SAR) target image acquisition presents challenges and incurs high annotation costs. The emergence of self-supervised contrastive learning shows promise for SAR automatic target recognition (ATR) with limited data. However, SAR images suffer from poor discriminability and high sample similarity, hindering instance discrimination in contrastive learning. To address this, we propose Low-confidence Discriminant Contrastive Learning (LDCL), which integrates group-instance contrast and batch mixed training for SAR ATR. LDCL consists of two branches: classical instance discrimination and group-instance discrimination. We refine the SAR-group instance discrimination loss function by incorporating distance calculations to guide feature vectors towards nearest clusters, enhancing discrimination within the feature space. Additionally, we introduce a batch image mixing training strategy to reduce confidence in SAR instance discrimination while preserving intraclass consistency. Experimental results on small sample MSTAR and FUSAR-Ship datasets demonstrate that LDCL outperforms traditional transfer learning and self-supervised learning methods, achieving significantly higher recognition rates in SAR ATR tasks.

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Section: A Small Sample Sar Atrmentioning

confidence: 99%

LDCL: Low-Confidence Discriminant Contrastive Learning for Small-Sample SAR ATR

Liao,

Zhai,

Wang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…In recent decades, deep learning has been applied in signal and image processing fields and demonstrated its superior performance. As for the SAR ATR application, many excellent studies have proposed many deep learning methods with outstanding results [14][15][16][17][18][19][20][21][22][23][24][25]. Chen et al [26] proposed an all-convolutional network replacing all the dense layers with the convolutional layers, which leads to outstanding recognition performance.…”

Section: Introductionmentioning

confidence: 99%

When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation

et al. 2020

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With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation.

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