{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:44:17Z","timestamp":1760233457930,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T00:00:00Z","timestamp":1610582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of China","award":["Y7K00100KJ,Y930060K8M"],"award-info":[{"award-number":["Y7K00100KJ,Y930060K8M"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As one of the most important active remote sensing technologies, synthetic aperture radar (SAR) provides advanced advantages of all-day, all-weather, and strong penetration capabilities. Due to its unique electromagnetic spectrum and imaging mechanism, the dimensions of remote sensing data have been considerably expanded. Important for fundamental research in microwave remote sensing, SAR image classification has been proven to have great value in many remote sensing applications. Many widely used SAR image classification algorithms rely on the combination of hand-designed features and machine learning classifiers, which still experience many issues that remain to be resolved and overcome, including optimized feature representation, the fuzzy confusion of speckle noise, the widespread applicability, and so on. To mitigate some of the issues and to improve the pattern recognition of high-resolution SAR images, a ConvCRF model combined with superpixel boundary constraint is developed. The proposed algorithm can successfully combine the local and global advantages of fully connected conditional random fields and deep models. An optimizing strategy using a superpixel boundary constraint in the inference iterations more efficiently preserves structure details. The experimental results demonstrate that the proposed method provides competitive advantages over other widely used models. In the land cover classification experiments using the MSTAR, E-SAR and GF-3 datasets, the overall accuracy of our proposed method achieves 90.18 \u00b1 0.37, 91.63 \u00b1 0.27, and 90.91 \u00b1 0.31, respectively. Regarding the issues of SAR image classification, a novel integrated learning containing local and global image features can bring practical implications.<\/jats:p>","DOI":"10.3390\/rs13020271","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T01:33:29Z","timestamp":1610674409000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["SAR Image Classification Using Fully Connected Conditional Random Fields Combined with Deep Learning and Superpixel Boundary Constraint"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhensheng","family":"Sun","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Miao","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3292-8551","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"China Aerospace Science and Industry Corporation Limited, Beijing 100854, China"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}]},{"given":"Xingfa","family":"Gu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Xiaofei","family":"Mi","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-8014","authenticated-orcid":false,"given":"Weijia","family":"Cao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zhouwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mori, S., Polverari, F., Mereu, L., Pulvirenti, L., Montopoli, M., Pierdicca, N., and Marzano, F.S. (2015, January 26\u201331). Atmospheric precipitation impact on synthetic aperture radar imagery: Numerical model at X and KA bands. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326085"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1109\/TGRS.2018.2809504","article-title":"Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR with Validation by Airborne Measurements","volume":"56","author":"Singha","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2008.2003127","article-title":"On the Mueller Scattering Matrix for SAR Sea Oil Slick Observation","volume":"5","author":"Nunziata","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2019). Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11151762"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2017.2772349","article-title":"A Framework of Rapid Regional Tsunami Damage Recognition from Post-event TerraSAR-X Imagery Using Deep Neural Networks","volume":"15","author":"Bai","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1109\/JSTARS.2018.2803260","article-title":"SAR Image Land Cover Datasets for Classification Benchmarking of Temporal Changes","volume":"11","author":"Dumitru","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5256","DOI":"10.1109\/TGRS.2013.2287712","article-title":"Land Cover and Soil Type Mapping from Spaceborne PolSAR Data at L-Band with Probabilistic Neural Network","volume":"52","author":"Antropov","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","unstructured":"Sun, Z., Li, J., Liu, P., Cao, W., Yu, T., and Gu, X. (2020). SAR Image Classification Using Greedy Hierarchical Learning with Unsupervised Stacked CAEs. IEEE Trans. Geosci. Remote Sens., 1\u201319."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/36.406698","article-title":"Monitoring of rice crop growth from space using the ERS-1 C-band SAR","volume":"33","author":"Kurosu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TGRS.2011.2176740","article-title":"Rice Phenology Monitoring by Means of SAR Polarimetry at X-Band","volume":"50","author":"Cloude","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/TGRS.2005.857897","article-title":"Operational map-guided classification of SAR sea ice imagery","volume":"43","author":"Maillard","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/LGRS.2010.2058997","article-title":"Multilevel Local Pattern Histogram for SAR Image Classification","volume":"8","author":"Dai","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","unstructured":"Fletcher, N.D., and Evans, A.N. (2002, January 24\u201328). Minimum distance texture classification of SAR images using wavelet packets. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2442","DOI":"10.1109\/TGRS.2016.2645226","article-title":"Deep Supervised and Contractive Neural Network for SAR Image Classification","volume":"55","author":"Geng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/JSTARS.2018.2851023","article-title":"Patch-Sorted Deep Feature Learning for High Resolution SAR Image Classification","volume":"11","author":"Ren","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Geng, J., Wang, H., Fan, J., and Ma, X. (2017, January 19\u201321). Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958819"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, Y., Sun, Z., Qu, R., Jiao, L., Liu, F., and Zhang, X. (2020). Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12101694"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_24","unstructured":"Breiman, L. (2001). Random Forests, Kluwer Academic Publishers."},{"key":"ref_25","first-page":"1189","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th international conference on Machine learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_29","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems\u2014Volume 2, Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H.S. (2015, January 11\u201318). Conditional Random Fields as Recurrent Neural Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dumitru, C.O., Schwarz, G., Cui, S., and Datcu, M. (2016, January 23\u201325). Improved image classification by proper patch size selection: TerraSAR-X vs. Sentinel-1A. Proceedings of the 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502739"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tabti, S., Deledalle, C., Denis, L., and Tupin, F. (2015, January 26\u201331). Patch-based SAR image classification: The potential of modeling the statistical distribution of patches with Gaussian mixtures. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326286"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/LGRS.2010.2073678","article-title":"Unsupervised Classification of SAR Images Using Markov Random Fields and G0 Model","volume":"8","author":"Picco","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4933","DOI":"10.1109\/TGRS.2015.2413905","article-title":"Hierarchical Conditional Random Fields Model for Semisupervised SAR Image Segmentation","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","unstructured":"Kr\u00e4henb\u00fchl, P., and Koltun, V. (2011, January 12\u201315). Efficient inference in fully connected CRFs with Gaussian edge potentials. Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain."},{"key":"ref_38","first-page":"228","article-title":"MSTAR extended operating conditions\u2014A tutorial","volume":"2757","author":"Keydel","year":"1996","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vineet, V., Warrell, J., and Torr, P.H.S. (2012, January 7\u201313). Filter-Based mean-field inference for random fields with higher-order terms and product label-spaces. Proceedings of the 12th European conference on Computer Vision\u2014Volume Part V, Florence, Italy.","DOI":"10.1007\/978-3-642-33715-4_3"},{"key":"ref_40","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems\u2014Volume 1, Lake Tahoe, Nevada."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:11:11Z","timestamp":1760159471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,14]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020271"],"URL":"https:\/\/doi.org\/10.3390\/rs13020271","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,1,14]]}}}