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Project","award":["2220002000246"],"award-info":[{"award-number":["2220002000246"]}]},{"name":"Guangdong Jiangmen Science and Technology Research Project","award":["2023760300070008390"],"award-info":[{"award-number":["2023760300070008390"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The utilization of deep learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has witnessed a recent surge owing to its remarkable feature extraction capabilities. Nonetheless, deep learning methodologies are often encumbered by inadequacies in labeled data and the protracted nature of training processes. To address these challenges and offer an alternative avenue for accurately extracting image features, this paper puts forth a novel and distinctive network dubbed the Capsule Broad Learning System Network for robust SAR ATR (CBLS-SARNET). This novel strategy is specifically tailored to cater to small-sample SAR ATR scenarios. On the one hand, we introduce a United Division Co-training (UDC) Framework as a feature filter, adeptly amalgamating CapsNet and the Broad Learning System (BLS) to enhance network efficiency and efficacy. On the other hand, we devise a Parameters Sharing (PS) network to facilitate secondary learning by sharing the weight and bias of BLS node layers, thereby augmenting the recognition capability of CBLS-SARNET. Experimental results unequivocally demonstrate that our proposed CBLS-SARNET outperforms other deep learning methods in terms of recognition accuracy and training time. Furthermore, experiments validate the generalization and robustness of our novel method under various conditions, including the addition of blur, Gaussian noise, noisy labels, and different depression angles. These findings underscore the superior generalization capabilities of CBLS-SARNET across diverse SAR ATR scenarios.<\/jats:p>","DOI":"10.3390\/rs16091526","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T03:23:47Z","timestamp":1714101827000},"page":"1526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Capsule Broad Learning System Network for Robust Synthetic Aperture Radar Automatic Target Recognition with Small Samples"],"prefix":"10.3390","volume":"16","author":[{"given":"Cuilin","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Yikui","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Wuyi University, Jiangmen 529020, China"}]},{"given":"Haifeng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Qingsong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7500-5581","authenticated-orcid":false,"given":"Wenlve","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Wuyi University, Jiangmen 529020, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, C., Dong, H., and Deng, B. (2023). Improving Pre-Training and Fine-Tuning for Few-Shot SAR Automatic Target Recognition. Remote Sens., 15.","DOI":"10.3390\/rs15061709"},{"key":"ref_2","first-page":"5216210","article-title":"Label Noise Modeling and Correction via Loss Curve Fitting for SAR ATR","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, M., Cheng, J., Lei, T., Feng, Z., Zhou, X., Liu, Y., and Chen, Z. (2023). C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR. Remote Sens., 15.","DOI":"10.20944\/preprints202304.0714.v1"},{"key":"ref_4","first-page":"4018305","article-title":"Novel Loss Function in CNN for Small Sample Target Recognition in SAR Images","volume":"19","author":"Zhou","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3560","DOI":"10.1109\/JSTARS.2023.3347454","article-title":"Multilevel Pyramid Feature Extraction and Task Decoupling Network for SAR Ship Detection","volume":"17","author":"Li","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1109\/TGRS.2019.2957453","article-title":"LDGAN: A Synthetic Aperture Radar Image Generation Method for Automatic Target Recognition","volume":"58","author":"Cao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/JSTARS.2021.3068944","article-title":"Training SAR-ATR Models for Reliable Operation in Open-World Environments","volume":"14","author":"Inkawhich","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/LGRS.2019.2958379","article-title":"SAR Target Recognition with Limited Training Data Based on Angular Rotation Generative Network","volume":"17","author":"Sun","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5204017","DOI":"10.1109\/TGRS.2021.3065461","article-title":"Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks","volume":"60","author":"Molini","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images","volume":"47","author":"Gao","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4782","DOI":"10.1109\/JSTARS.2022.3181744","article-title":"An Object-Oriented CNN Model Based on Improved Superpixel Segmentation for High-Resolution Remote Sensing Image Classification","volume":"15","author":"Li","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","first-page":"4701914","article-title":"Self-Trained Target Detection of Radar and Sonar Images Using Automatic Deep Learning","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6789","DOI":"10.1109\/TIP.2020.2993931","article-title":"Group Feedback Capsule Network","volume":"29","author":"Ding","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6466","DOI":"10.1109\/TII.2020.2964117","article-title":"A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault Diagnosis","volume":"16","author":"Huang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TGRS.2017.2748120","article-title":"Diversity-Promoting Deep Structural Metric Learning for Remote Sensing Scene Classification","volume":"56","author":"Gong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tan, Z., Lv, Q., Li, J., Zhu, B., and Liu, Y. (2024). An Efficient Hybrid CNN-Transformer Approach for Remote Sensing Super-Resolution. Remote Sens., 16.","DOI":"10.3390\/rs16050880"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/TSMC.2020.2995205","article-title":"Analysis and Variants of Broad Learning System","volume":"52","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","article-title":"Broad Learning System: An Effective and Efficient Incremental Learning System without the Need for Deep Architecture","volume":"29","author":"Chen","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/TNNLS.2018.2866622","article-title":"Universal Approximation Capability of Broad Learning System and Its Structural Variations","volume":"30","author":"Chen","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6074","DOI":"10.1109\/TSMC.2019.2957818","article-title":"A Construction of Robust Representations for Small Data Sets Using Broad Learning System","volume":"51","author":"Tang","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TSMC.2020.3043147","article-title":"Stacked Broad Learning System: From Incremental Flatted Structure to Deep Model","volume":"51","author":"Liu","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1109\/TKDE.2018.2866149","article-title":"Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction","volume":"31","author":"Han","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1109\/TCSI.2019.2959886","article-title":"Semi-Supervised Broad Learning System Based on Manifold Regularization and Broad Network","volume":"67","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Regul. Pap."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7363","DOI":"10.1109\/TSMC.2020.2967936","article-title":"Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment","volume":"51","author":"Sharma","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7266","DOI":"10.1109\/TIP.2021.3104179","article-title":"Rotation Awareness Based Self-Supervised Learning for SAR Target Recognition with Limited Training Samples","volume":"30","author":"Wen","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/3477.740166","article-title":"A Rapid Learning and Dynamic Stepwise Updating Algorithm for Flat Neural Networks and the Application to Time-Series Prediction","volume":"29","author":"Chen","year":"1999","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/72.536316","article-title":"A Rapid Supervised Learning Neural Network for Function Interpolation and Approximation","volume":"7","author":"Chen","year":"1996","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1109\/JSTARS.2023.3270141","article-title":"Remote Sensing Based Crop Type Classification Via Deep Transfer Learning","volume":"16","author":"Gadiraju","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5869","DOI":"10.1109\/TIP.2022.3201602","article-title":"Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training","volume":"31","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5649","DOI":"10.1109\/TIP.2019.2921882","article-title":"Channel Splitting Network for Single MR Image Super-Resolution","volume":"28","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qiao, S., Shen, W., Zhang, Z., Wang, B., and Yuille, A. (2018, January 8\u201314). Deep Co-Training for Semi-Supervised Image Recognition. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TNNLS.2019.2935033","article-title":"Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling","volume":"31","author":"Chu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1109\/TIP.2019.2948480","article-title":"Class-Specific Reconstruction Transfer Learning for Visual Recognition Across Domains","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10213","DOI":"10.1109\/JSTARS.2021.3116979","article-title":"SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm","volume":"14","author":"Feng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20\u201325). RepVGG: Making VGG-Style ConvNets Great Again. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, W., Xie, D., Zhang, Y., and Pu, S. (2019, January 15\u201320). All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00741"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4911","DOI":"10.1109\/TIP.2020.2975718","article-title":"A Multiple-Instance Densely-Connected ConvNet for Aerial Scene Classification","volume":"29","author":"Bi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5629217","DOI":"10.1109\/TGRS.2022.3201755","article-title":"All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification","volume":"60","author":"Bi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target Classification Using the Deep Convolutional Networks for SAR Images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1526\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:33:32Z","timestamp":1760106812000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":46,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091526"],"URL":"https:\/\/doi.org\/10.3390\/rs16091526","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}