{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T19:00:24Z","timestamp":1789066824879,"version":"build-2803163510"},"reference-count":124,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC","award":["42201394"],"award-info":[{"award-number":["42201394"]}]},{"name":"NSFC","award":["12326615"],"award-info":[{"award-number":["12326615"]}]},{"name":"NSFC","award":["2022YFA1003800"],"award-info":[{"award-number":["2022YFA1003800"]}]},{"name":"NSFC","award":["PCL2024A06"],"award-info":[{"award-number":["PCL2024A06"]}]},{"name":"NSFC","award":["QCYRCXM-2022-30"],"award-info":[{"award-number":["QCYRCXM-2022-30"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42201394"],"award-info":[{"award-number":["42201394"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["12326615"],"award-info":[{"award-number":["12326615"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFA1003800"],"award-info":[{"award-number":["2022YFA1003800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["PCL2024A06"],"award-info":[{"award-number":["PCL2024A06"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["QCYRCXM-2022-30"],"award-info":[{"award-number":["QCYRCXM-2022-30"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Key Project of PCL","award":["42201394"],"award-info":[{"award-number":["42201394"]}]},{"name":"Major Key Project of PCL","award":["12326615"],"award-info":[{"award-number":["12326615"]}]},{"name":"Major Key Project of PCL","award":["2022YFA1003800"],"award-info":[{"award-number":["2022YFA1003800"]}]},{"name":"Major Key Project of PCL","award":["PCL2024A06"],"award-info":[{"award-number":["PCL2024A06"]}]},{"name":"Major Key Project of PCL","award":["QCYRCXM-2022-30"],"award-info":[{"award-number":["QCYRCXM-2022-30"]}]},{"name":"Qinchuangyuan High-level Innovation and Entrepreneurial Talent Program","award":["42201394"],"award-info":[{"award-number":["42201394"]}]},{"name":"Qinchuangyuan High-level Innovation and Entrepreneurial Talent Program","award":["12326615"],"award-info":[{"award-number":["12326615"]}]},{"name":"Qinchuangyuan High-level Innovation and Entrepreneurial Talent Program","award":["2022YFA1003800"],"award-info":[{"award-number":["2022YFA1003800"]}]},{"name":"Qinchuangyuan High-level Innovation and Entrepreneurial Talent Program","award":["PCL2024A06"],"award-info":[{"award-number":["PCL2024A06"]}]},{"name":"Qinchuangyuan High-level Innovation and Entrepreneurial Talent Program","award":["QCYRCXM-2022-30"],"award-info":[{"award-number":["QCYRCXM-2022-30"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs16244632","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T06:44:05Z","timestamp":1733899445000},"page":"4632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Survey on Deep Learning for Few-Shot PolSAR Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1567-3091","authenticated-orcid":false,"given":"Ningwei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6656-6061","authenticated-orcid":false,"given":"Weiqiang","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3629-0332","authenticated-orcid":false,"given":"Haixia","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Department of Mathematics and Fundamental Research, Peng Cheng Laboratory, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghuai","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shi, C., Zhang, X., Sun, J., and Wang, L. (2022). Remote sensing scene image classification based on self-compensating convolution neural network. Remote Sens., 14.","DOI":"10.3390\/rs14030545"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6362","DOI":"10.1109\/TGRS.2020.2976694","article-title":"Automatic design of CNNs via differentiable neural architecture search for PolSAR image classification","volume":"58","author":"Dong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bi, H., Xu, F., Wei, Z., Han, Y., Cui, Y., Xue, Y., and Xu, Z. (August, January 28). Unsupervised PolSAR image factorization with deep convolutional networks. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900489"},{"key":"ref_5","first-page":"1","article-title":"PolSAR Ship Targets Generation via the Polarimetric Feature Guided Denoising Diffusion Probabilistic Model","volume":"21","author":"Liu","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wan, H., Tang, P., Tian, B., Yu, H., Jin, C., Zhao, B., and Wang, H. (2023). Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network. Appl. Sci., 13.","DOI":"10.3390\/app13042610"},{"key":"ref_7","first-page":"1","article-title":"SemiPSCN: Polarization Semantic Constraint Network for Semi-Supervised Segmentation in Large-Scale and Complex-Valued PolSAR Images","volume":"62","author":"Zeng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/JSTARS.2024.3418551","article-title":"Rank Learning Based Full-Resolution Quality Evaluation Method for Pansharpened Images","volume":"17","author":"Guan","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hua, W., Wang, Y., Yang, S., and Jin, X. (2024). PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network. Remote Sens., 16.","DOI":"10.3390\/rs16020296"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.1007\/s40808-023-01700-x","article-title":"Modeling PolSAR classification using convolutional neural network with homogeneity based kernel selection","volume":"9","author":"Parikh","year":"2023","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6623","DOI":"10.1109\/TGRS.2020.2978268","article-title":"A patch-to-pixel convolutional neural network for small ship detection with PolSAR images","volume":"58","author":"Jin","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.isprsjprs.2023.07.006","article-title":"Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection","volume":"202","author":"Gao","year":"2023","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, K., Kuang, Z., Jing, S., Bi, H., Li, Y., and Xu, C. (2024, January 7\u201312). Diffusion-Based Generative Self-Supervised Model for Few-Shot PolSAR Image Classification. Proceedings of the IGARSS 2024\u20132024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10642798"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3403100","article-title":"Polarimetry-inspired Contrastive Learning for Class-imbalanced PolSAR Image Classification","volume":"62","author":"Kuang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02757259409532206","article-title":"Speckle filtering of synthetic aperture radar images: A review","volume":"8","author":"Lee","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_16","first-page":"108","article-title":"Digital processing of synthetic aperture radar data","volume":"1","author":"Cumming","year":"2005","journal-title":"Artech House"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","unstructured":"Jolliffe, I.T. (2002). Principal Component Analysis for Special Types of Data, Springer."},{"key":"ref_20","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_21","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_23","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/TGRS.2003.815240","article-title":"Polarimetric SAR speckle noise model","volume":"41","author":"Fabregas","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"1","article-title":"MP-ResNet: Multipath residual network for the semantic segmentation of high-resolution PolSAR images","volume":"19","author":"Ding","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Karachristos, K., Koukiou, G., and Anastassopoulos, V. (2024). Fusion of Coherent and Non-Coherent Pol-SAR Features for Land Cover Classification. Electronics, 13.","DOI":"10.3390\/electronics13030634"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3038405","article-title":"Unsupervised deep representation learning and few-shot classification of PolSAR images","volume":"60","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., and Hospedales, T.M. (2018, January 18\u201323). Learning to compare: Relation network for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_30","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, N., Bi, H., Li, F., Xu, C., and Gao, J. (2023). Self-distillation-based polarimetric image classification with noisy and sparse labels. Remote Sens., 15.","DOI":"10.3390\/rs15245751"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gui, R., Xu, X., Wang, L., Yang, R., and Pu, F. (2018). A generalized zero-shot learning framework for PolSAR land cover classification. Remote Sens., 10.","DOI":"10.3390\/rs10081307"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3476089","article-title":"Cross-Attention-Driven Adaptive Graph Relational Network for Multi-Label Remote Sensing Scene Classification","volume":"62","author":"Bi","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/AERO.1996.499405","article-title":"Digital terrain elevation mapping system","volume":"Volume 4","author":"Malliot","year":"1996","journal-title":"Proceedings of the 1996 IEEE Aerospace Applications Conference. Proceedings"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., and Belongie, S. (2019, January 15\u201320). Class-balanced loss based on effective number of samples. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Larra\u00f1aga, A., and \u00c1lvarez-Mozos, J. (2016). On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery. Remote Sens., 8.","DOI":"10.3390\/rs8040335"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_39","first-page":"1","article-title":"Pol-SAR image classification using multifarious stratification stratagem in machine learning","volume":"37","author":"Ashwin","year":"2024","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ioannidou, M., Koukos, A., Sitokonstantinou, V., Papoutsis, I., and Kontoes, C. (2022). Assessing the added value of Sentinel-1 PolSAR data for crop classification. Remote Sens., 14.","DOI":"10.3390\/rs14225739"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yuan, J., Lv, X., Dou, F., and Yao, J. (2019). Change analysis in urban areas based on statistical features and temporal clustering using TerraSAR-X time-series images. Remote Sens., 11.","DOI":"10.3390\/rs11080926"},{"key":"ref_42","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/19479832.2019.1655489","article-title":"Classification of SAR and PolSAR images using deep learning: A review","volume":"11","author":"Parikh","year":"2020","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, Y., Wu, Z., Chen, J., Huang, Z., and Yang, L. (2024). Cross-Domain PolSAR Image Classification Using Complex-Valued Few-Shot Learning Network. IEEE Trans. Aerosp. Electron. Syst., 1\u201316.","DOI":"10.1109\/TAES.2024.3471490"},{"key":"ref_45","first-page":"3637","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lu, Y., Yang, C., and Meng, Z. (2021). Lithology discrimination using Sentinel-1 dual-pol data and SRTM data. Remote Sens., 13.","DOI":"10.3390\/rs13071280"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.09.002","article-title":"Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework","volume":"157","author":"Zhang","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Z., Qiu, X., and Zhang, Z. (2024). Global Polarimetric Synthetic Aperture Radar Image Segmentation with Data Augmentation and Hybrid Architecture Model. Remote Sens., 16.","DOI":"10.3390\/rs16020380"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced spectral classifiers for hyperspectral images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1109\/TNNLS.2018.2885799","article-title":"Task-oriented GAN for PolSAR image classification and clustering","volume":"30","author":"Liu","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_52","first-page":"1","article-title":"N-Cluster loss and hard sample generative deep metric learning for PolSAR image classification","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xie, W., Yang, X., Wang, R., and Zhao, F. (2022, January 17\u201322). Polsar Image Classification Via Auxiliary Classifier Generative Adversarial Network. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884179"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Dong, H., Si, L., Qiang, W., Miao, W., Zheng, C., Wu, Y., and Zhang, L. (2023). A polarimetric scattering characteristics-guided adversarial learning approach for unsupervised PolSAR image classification. Remote Sens., 15.","DOI":"10.3390\/rs15071782"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1109\/LGRS.2017.2758900","article-title":"Zero-shot learning of SAR target feature space with deep generative neural networks","volume":"14","author":"Song","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, H., Xing, C., Yin, J., and Yang, J. (2022). Land cover classification for polarimetric SAR images based on vision transformer. Remote Sens., 14.","DOI":"10.3390\/rs14184656"},{"key":"ref_57","unstructured":"Cattoi, A., Bruzzone, L., and Haensch, R. (2022, January 25\u201327). Transcoding-based pre-training of semantic segmentation networks for PolSAR images. Proceedings of the EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, VDE, Leipzig, Germany."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Han, P., Men, L., and Chen, Z. (2022, January 17\u201319). PolSAR Image Classification Based on Deep Features. Proceedings of the China High Resolution Earth Observation Conference, Changsha, China.","DOI":"10.1007\/978-981-19-8202-6_35"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6521","DOI":"10.1080\/01431161.2024.2391101","article-title":"Channel adaptive CVFCN using a new transfer method for PolSAR terrain classification","volume":"45","author":"Xie","year":"2024","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/JSTARS.2021.3134766","article-title":"Generalized zero-shot domain adaptation for unsupervised cross-domain PolSAR image classification","volume":"15","author":"Gui","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3073738","article-title":"A general feature paradigm for unsupervised cross-domain PolSAR image classification","volume":"19","author":"Gui","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1109\/TGRS.2019.2952236","article-title":"Online active extreme learning machine with discrepancy sampling for PolSAR classification","volume":"58","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","first-page":"1","article-title":"A Novel Causal Inference Guided Feature Enhancement Framework for PolSAR Image Classification","volume":"62","author":"Dong","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Qin, X., Yang, J., Li, P., Sun, W., and Liu, W. (2019). A novel relational-based transductive transfer learning method for PolSAR images via time-series clustering. Remote Sens., 11.","DOI":"10.3390\/rs11111358"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3192","DOI":"10.1109\/JSTARS.2020.3000743","article-title":"Eigenvalue statistical components-based PU-learning for PolSAR built-up areas extraction and cross-domain analysis","volume":"13","author":"Gui","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_66","first-page":"1","article-title":"A CA_Based Weighted Clustering Adversarial Network for Unsupervised Domain Adaptation PolSAR Image Classification","volume":"20","author":"Hua","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1049\/icp.2024.1345","article-title":"Complex-valued cross-domain few-shot learning network for PolSAR image classification","volume":"Volume 2023","author":"Cao","year":"2023","journal-title":"IET Conference Proceedings CP874"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sun, W., Li, P., Yang, J., Shi, L., and Zhao, L. (2017, January 23\u201328). Domain adaptation for polsar land classification using linear discriminative Laplacian Eigenmaps. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127697"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sun, W., Li, P., Du, B., Yang, J., Tian, L., Li, M., and Zhao, L. (2020). Scatter matrix based domain adaptation for bi-temporal polarimetric SAR images. Remote Sens., 12.","DOI":"10.3390\/rs12040658"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9224","DOI":"10.1109\/TGRS.2020.3048967","article-title":"A mutual information-based self-supervised learning model for PolSAR land cover classification","volume":"59","author":"Ren","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","first-page":"1","article-title":"MLR-SimSiam: A Contrastive Pre-training Model based on Polarimetric Jittering and Mutual Learning Regularizer for PolSAR Image Classification","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kuang, Z., Bi, H., and Li, F. (2023, January 16\u201321). Complex-valued self-supervised PolSAR image classification integrating attention mechanism. Proceedings of the IGARSS 2023\u20132023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10281737"},{"key":"ref_73","first-page":"1","article-title":"Exploring PolSAR images representation via self-supervised learning and its application on few-shot classification","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Qiu, W., Pan, Z., and Yang, J. (2023). Few-shot polsar ship detection based on polarimetric features selection and improved contrastive self-supervised learning. Remote Sens., 15.","DOI":"10.3390\/rs15071874"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wang, Y., Guo, Z., Bi, H., Hong, D., and Xu, C. (2024, January 7\u201312). Dual-Branch PolSAR Image Classification Based On Graphmae And Local Feature Extraction. Proceedings of the IGARSS 2024\u20132024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10642812"},{"key":"ref_76","first-page":"1","article-title":"Exploring vision transformers for polarimetric SAR image classification","volume":"60","author":"Dong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Cai, J., Ma, Y., Feng, Z., and Yang, S. (2024). Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification. arXiv.","DOI":"10.1109\/JSTARS.2024.3429538"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1049\/rsn2.12484","article-title":"A novel self-supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images","volume":"18","author":"Darvishnezhad","year":"2024","journal-title":"IET Radar Sonar Navig."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Ni, J., Jia, Y., Yin, Q., Zhou, Y., and Zhang, F. (26\u20132, January 26). Metric learning based fine-grained classification for PolSAR imagery. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Virtual.","DOI":"10.1109\/IGARSS39084.2020.9323087"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3505988","article-title":"Meta-Graph Representative Learning for PolSAR Image Classification","volume":"62","author":"Yang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhang, P., Liu, C., Chang, X., Li, Y., and Li, M. (2021, January 15\u201319). Metric-based meta-learning model for few-shot PolSAR image terrain classification. Proceedings of the 2021 CIE International Conference on Radar (Radar), Haikou, China.","DOI":"10.1109\/Radar53847.2021.10027883"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"108922","DOI":"10.1016\/j.asoc.2022.108922","article-title":"Spatial feature-based convolutional neural network for PolSAR image classification","volume":"123","author":"Shang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Dong, H., Xu, X., Gui, R., Song, C., and Sui, H. (2016, January 10\u201315). Metric learning based collapsed building extraction from post-earthquake PolSAR imagery. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730237"},{"key":"ref_84","first-page":"1","article-title":"A Novel Feature Evaluation Method in Mapping Forest AGB by Fusing Multiple Evaluation Metrics Using PolSAR Data","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Hua, W., Zhang, Y., Zhang, C., and Jin, X. (2023). PolSAR image classification based on relation network with SWANet. Remote Sens., 15.","DOI":"10.3390\/rs15082025"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1109\/JSTARS.2019.2913445","article-title":"Fully polarized SAR imagery classification based on deep reinforcement learning method using multiple polarimetric features","volume":"12","author":"Huang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_87","first-page":"1","article-title":"Adaptive graph convolutional network for PolSAR image classification","volume":"60","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_88","first-page":"423","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Ahuja","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Quan, Y., Zhang, R., Li, J., Ji, S., Guo, H., and Yu, A. (2024). Learning SAR-Optical Cross Modal Features for Land Cover Classification. Remote Sens., 16.","DOI":"10.3390\/rs16020431"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Dong, Y., and H\u00e4nsch, R. (2023, January 16\u201321). Multimodal self-supervised learning for semantic analysis of PolSAR imagery. Proceedings of the IGARSS 2023\u20132023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10283301"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Li, F., Zhang, C., Zhang, X., and Li, Y. (2023). MF-DCMANet: A multi-feature dual-stage cross manifold attention network for PolSAR target recognition. Remote Sens., 15.","DOI":"10.3390\/rs15092292"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1109\/TIV.2023.3234583","article-title":"Cross-modal supervision-based multitask learning with automotive radar raw data","volume":"8","author":"Jin","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2020.06.014","article-title":"X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data","volume":"167","author":"Hong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3335484","article-title":"Cross-modal graph knowledge representation and distillation learning for land cover classification","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3506602","article-title":"Deep Symmetric Fusion Transformer for Multimodal Remote Sensing Data Classification","volume":"62","author":"Chang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"7861","DOI":"10.1080\/01431161.2018.1479786","article-title":"PolSAR image classification via multimodal sparse representation-based feature fusion","volume":"39","author":"Ren","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1049\/rsn2.12389","article-title":"Feature fusion method based on local binary graph for PolSAR image classification","volume":"17","author":"Sebt","year":"2023","journal-title":"IET Radar Sonar Navig."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Shi, J., Nie, M., Ji, S., Shi, C., Liu, H., and Jin, H. (2023). Polarimetric SAR Image Classification Based on Double-Channel Convolution Network and Edge-preserving Markov Random Field. Remote Sens., preprints.","DOI":"10.20944\/preprints202310.0166.v1"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zhang, Q., Chen, W., and Chen, C. (2021, January 22\u201324). PolSAR Image Classification Based on Resblock Combined with Attention Model. Proceedings of the 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), Nanjing, China.","DOI":"10.1109\/ICSIP52628.2021.9688779"},{"key":"ref_100","first-page":"1","article-title":"Polarimetric SAR image classification based on feature enhanced superpixel hypergraph neural network","volume":"60","author":"Geng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3727","DOI":"10.1109\/JSTARS.2024.3355290","article-title":"Multiscale superpixel-guided weighted graph convolutional network for polarimetric SAR image classification","volume":"17","author":"Wang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3239263","article-title":"Local window attention transformer for polarimetric SAR image classification","volume":"20","author":"Jamali","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Ren, B., Chen, M., Hou, B., Hong, D., Ma, S., Chanussot, J., and Jiao, L. (2022). PolSAR Scene Classification via Low-Rank Constrained Multimodal Tensor Representation. Remote Sens., 14.","DOI":"10.3390\/rs14133117"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Liu, X., Samat, A., Li, E., Wang, W., and Abuduwaili, J. (2022). Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries. Sensors, 22.","DOI":"10.3390\/s22186844"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2024.3417929","article-title":"A Feature Fusion Network for PolSAR Image Classification Based on Physical Features and Deep Features","volume":"21","author":"Hua","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, W., Chen, W., Chen, C., and Liang, Z. (2024). MFFnet: Multimodal Feature Fusion Network for Synthetic Aperture Radar and Optical Image Land Cover Classification. Remote Sens., 16.","DOI":"10.3390\/rs16132459"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4982","DOI":"10.1109\/JSTARS.2018.2881342","article-title":"Remote sensing image fusion using hierarchical multimodal probabilistic latent semantic analysis","volume":"11","author":"Haut","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"8796","DOI":"10.1109\/TGRS.2019.2922978","article-title":"CNN-based polarimetric decomposition feature selection for PolSAR image classification","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_109","first-page":"1","article-title":"DFAF-Net: A dual-frequency PolSAR image classification network based on frequency-aware attention and adaptive feature fusion","volume":"60","author":"Cao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"8928","DOI":"10.1109\/JSTARS.2022.3213601","article-title":"SDCAFNet: A deep convolutional neural network for land-cover semantic segmentation with the fusion of PolSAR and optical images","volume":"15","author":"Chu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.neucom.2020.01.020","article-title":"PolSAR image classification via a novel semi-supervised recurrent complex-valued convolution neural network","volume":"388","author":"Xie","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_112","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: Proceedings of the 18th International Conference, Munich, Germany, 5\u20139 October 2015, Springer. Proceedings, Part III 18."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H. (2021, January 20\u201325). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_114","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the International conference on machine learning. PMLR, Virtual."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Chen, X., and He, K. (2021, January 20\u201325). Exploring simple siamese representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Chen, D., Chen, Y., Li, Y., Mao, F., He, Y., and Xue, H. (2021, January 6\u201312). Self-supervised learning for few-shot image classification. Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual.","DOI":"10.1109\/ICASSP39728.2021.9413783"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","article-title":"PolSAR image classification using polarimetric-feature-driven deep convolutional neural network","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3185118","article-title":"PolSAR image classification based on deep convolutional neural networks using wavelet transformation","volume":"19","author":"Jamali","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"8583","DOI":"10.1109\/TGRS.2020.2988982","article-title":"Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.1109\/JSTARS.2022.3168799","article-title":"DNN-based PolSAR image classification on noisy labels","volume":"15","author":"Ni","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_121","first-page":"1","article-title":"Superpixel-based cropland classification of SAR image with statistical texture and polarization features","volume":"19","author":"Chen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_122","first-page":"1","article-title":"Polarimetric multipath convolutional neural network for PolSAR image classification","volume":"60","author":"Cui","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","article-title":"Complex-valued convolutional neural network and its application in polarimetric SAR image classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3210948","article-title":"Semi-supervised classification of dual-frequency polsar image using joint feature learning and cross label-information network","volume":"60","author":"Xin","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4632\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:52:09Z","timestamp":1760115129000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4632"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":124,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244632"],"URL":"https:\/\/doi.org\/10.3390\/rs16244632","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,11]]}}}