{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:05:43Z","timestamp":1776092743807,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["61972240"],"award-info":[{"award-number":["61972240"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["20050501900"],"award-info":[{"award-number":["20050501900"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Commission","award":["61972240"],"award-info":[{"award-number":["61972240"]}]},{"name":"Shanghai Science and Technology Commission","award":["20050501900"],"award-info":[{"award-number":["20050501900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Distinguishing sea ice and water is crucial for safe navigation and carrying out offshore activities in ice zones. However, due to the complexity and dynamics of the ice\u2013water boundary, it is difficult for many deep learning-based segmentation algorithms to achieve accurate ice\u2013water segmentation in synthetic aperture radar (SAR) images. In this paper, we propose an ice\u2013water SAR segmentation network, E-MPSPNet, which can provide effective ice\u2013water segmentation by fusing semantic features and edge information. The E-MPSPNet introduces a multi-scale attention mechanism to better fuse the ice\u2013water semantic features and designs an edge supervision module (ESM) to learn ice\u2013water edge features. The ESM not only provides ice\u2013water edge prediction but also imposes constraints on the semantic feature extraction to better express the edge information. We also design a loss function that focuses on both ice\u2013water edges and semantic segmentations of ice and water for overall network optimization. With the AI4Arctic\/ASIP Sea Ice Dataset as the benchmark, experimental results show our E-MPSPNet achieves the best performance compared with other commonly used segmentation models, reaching 94.2% for accuracy, 93.0% for F-score, and 89.2% for MIoU. Moreover, our E-MPSPNet shows a relatively smaller model size and faster processing speed. The application of the E-MPSPNet for processing a SAR scene demonstrates its potential for operational use in drawing near real-time navigation charts of sea ice.<\/jats:p>","DOI":"10.3390\/rs14225753","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:32:16Z","timestamp":1668479536000},"page":"5753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["E-MPSPNet: Ice\u2013Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-5563","authenticated-orcid":false,"given":"Wei","family":"Song","sequence":"first","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Hongtao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Qi","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Guoping","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2773-4421","authenticated-orcid":false,"given":"Antonio","family":"Liotta","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","unstructured":"Carter, N., Dawson, J., Joyce, J., and Ogilvie, A. (2017). Arctic Corridors and Northern Voices: Governing Marine Transportation in the Canadian Arctic (Arviat, Nunavut Community Report), Arctic Corridors."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7711","DOI":"10.1109\/JSEN.2020.2981398","article-title":"Automatic SAR Image Registration via Tsallis Entropy and Iterative Search Process","volume":"20","author":"Kang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/LGRS.2011.2167211","article-title":"A Neighborhood-Based Ratio Approach for Change Detection in SAR Images","volume":"9","author":"Gong","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.2112\/SI102-010.1","article-title":"Land Subsidence Measurement of Jakarta Coastal Area Using Time Series Interferometry with Sentinel-1 SAR Data","volume":"102","author":"Hakim","year":"2020","journal-title":"J. Coast. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.1109\/TGRS.2009.2039577","article-title":"Dual-Polarization C-Band Radar Observations of Sea Ice in the Amundsen Gulf","volume":"48","author":"Partington","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1109\/TGRS.2002.806991","article-title":"Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice","volume":"40","author":"Makynen","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/07038992.2001.10854882","article-title":"Study of Multi-Polarization C-Band Backscatter Signatures for Arctic Sea Ice Mapping with Future Satellite SAR","volume":"27","author":"Nghiem","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_8","first-page":"113","article-title":"Textural Analysis and Real-Time Classification of Sea-Ice Types Using Digital SAR Data","volume":"2","author":"Holmes","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TGRS.2003.817218","article-title":"Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery","volume":"42","author":"Clausi","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4397","DOI":"10.1109\/TGRS.2012.2192278","article-title":"Operational SAR Sea-Ice Image Classification","volume":"50","author":"Ochilov","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Korosov, A., Zakhvatkina, N., and Muckenhuber, S. (2015). Ice\/Water Classification of Sentinel-1 Images. EGU General Assembly Conference Abstracts, EGU."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2014.2365215","article-title":"SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data","volume":"8","author":"Liu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.5194\/tc-11-33-2017","article-title":"Operational algorithm for ice\u2013water classification on dual-polarized RADARSAT-2 images","volume":"11","author":"Zakhvatkina","year":"2017","journal-title":"Cryosphere"},{"key":"ref_16","first-page":"239","article-title":"Landfast sea ice monitoring using multisensor fusion in the Antarctic","volume":"52","author":"Kim","year":"2015","journal-title":"Mapp. Sci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1017\/aog.2018.7","article-title":"Comparison of ice\/water classification in Fram Strait from C- and L-band SAR imagery","volume":"59","author":"Wiebke","year":"2018","journal-title":"Ann. Glaciol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/LGRS.2005.847930","article-title":"Open Water Detection from Baltic Sea Ice Radarsat-1 SAR Imagery","volume":"2","author":"Karvonen","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2019.03.015","article-title":"A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem","volume":"151","author":"Mohammadimanesh","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/JSTARS.2017.2755672","article-title":"OpenSARShip: A dataset dedicated to Sentinel-1 ship interpretation","volume":"11","author":"Huang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201322). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_22","first-page":"4300514","article-title":"HED-UNet: Combined segmentation and edge detection for monitoring the Antarctic coastline","volume":"60","author":"Heidler","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bai, L., Song, G., Zhang, J., Tao, J., Mulvenna, M., Bond, R., and Chen, L. (2021). An Oil Well Dataset Derived from Satellite-Based Remote Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13061132"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khaleghian, S., Ullah, H., Kr\u00e6mer, T., Hughes, N., Eltoft, T., and Marinoni, A. (2021). Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks. Remote Sens., 13.","DOI":"10.3390\/rs13091734"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Song, W., Gao, W., He, Q., Liotta, A., and Guo, W. (2022). SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay. Remote Sens., 14.","DOI":"10.3390\/rs14010168"},{"key":"ref_26","unstructured":"Wulf, T., Kreiner, M.B., Buus-Hinkler, J., Tonboe, R.T., H\u00f8yer, J.L., Saldo, R., Pedersen, L.T., Nielsen, A.A., Skriver, H., and Malmgren-Hansen, D. (2020, January 15\u201317). Fusion of Satellite SAR and Passive Microwave Radiometer Data for Automated Sea Ice Mapping and the Expected Impact of CIMR Observations. Proceedings of the From Science to Operations for the Copernicus Imaging Microwave Radiometer (CIMR) Mission, Noordwijk, The Netherlands."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/TGRS.2020.3004539","article-title":"A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion","volume":"59","author":"Pedersen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dirscherl, M., Dietz, A., Kneisel, C., and Kuenzer, C. (2021). A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning. Remote Sens., 13.","DOI":"10.5194\/egusphere-egu21-508"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kruk, R., Fuller, M., Komarov, A., Isleifson, D., and Jeffrey, I. (2020). Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12152486"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Han, Y., Liu, Y., Hong, Z., Zhang, Y., Yang, S., and Wang, J. (2021). Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040592"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, T., Yang, Y., Shokr, M., Mi, C., Li, X.-M., Cheng, X., and Hui, F. (2021). Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13081452"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Boulze, H., Korosov, A.A., and Brajard, J. (2020). Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12132165"},{"key":"ref_34","first-page":"357","article-title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs","volume":"4","author":"Chen","year":"2014","journal-title":"Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Barron, J.T., Papandreou, G., Murphy, K., and Yuille, A.L. (July, January 26). Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.492"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (November, January 27). Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00533"},{"key":"ref_37","first-page":"110","article-title":"The Analysis of Edge Detection Uncertainty of Remote Sensing Images and its Processing Method","volume":"32","author":"Sun","year":"2010","journal-title":"Remote Sens. Inf."},{"key":"ref_38","first-page":"675","article-title":"Cognitive physics-based method for image edge representation and extraction with uncertainty","volume":"62","author":"Wu","year":"2013","journal-title":"Acta Phys. Sin."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1109\/TGRS.2017.2765248","article-title":"Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel","volume":"56","author":"Park","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). 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","unstructured":"Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z. (2015, January 9\u201312). Deeply-Supervised Nets. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, San Diego, CA, USA."},{"key":"ref_43","first-page":"2999","article-title":"Focal Loss for Dense Object Detection","volume":"99","author":"Lin","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","unstructured":"Kingma, D.P., and Adam, J.B. (2014). A method for stochastic optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1080\/15481603.2018.1564499","article-title":"Semantic segmentation of high spatial resolution images with deep neural networks","volume":"56","author":"Yang","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4304013","DOI":"10.1109\/TGRS.2022.3149323","article-title":"AI4SeaIce: Toward Solving Ambiguous SAR Textures in Convolutional Neural Networks for Automatic Sea Ice Concentration Charting","volume":"60","author":"Stokholm","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104969","DOI":"10.1016\/j.cageo.2021.104969","article-title":"Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+","volume":"158","author":"Wang","year":"2022","journal-title":"Comput. Geosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"9941","DOI":"10.1109\/TGRS.2020.3035029","article-title":"Robustness of SAR Sea Ice Type Classification Across Incidence Angles and Seasons at L-Band","volume":"59","author":"Singha","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Singha, S., Johansson, A.M., and Doulgeris, A.P. (2021). Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sens., 13.","DOI":"10.3390\/rs13040552"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:58Z","timestamp":1760145478000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":49,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225753"],"URL":"https:\/\/doi.org\/10.3390\/rs14225753","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}