{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:54:09Z","timestamp":1775487249265,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Development Fund of Civil Aviation University of China","award":["2015\/6221042"],"award-info":[{"award-number":["2015\/6221042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper proposes an information enhancement network based on self-supervised learning (SEL-Net) for runway area detection. During the self-supervised learning phase, the distinctive attributes of PolSAR multi-channel data are fully harnessed to enhance the generated pretrained model\u2019s focus on airport runway areas. During the detection phase, this paper presents an improved U-Net detection network. Edge Feature Extraction Modules (EEM) are integrated into the encoder and skip connection sections, while Semantic Information Transmission Modules (STM) are embedded into the decoder section. Furthermore, improvements have been applied to the network\u2019s upsampling and downsampling architectures. Experimental results demonstrate that the proposed SEL-Net effectively addresses the issues of high false alarms and runway integrity, achieving a superior detection performance.<\/jats:p>","DOI":"10.3390\/rs15194708","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T08:58:17Z","timestamp":1695718697000},"page":"4708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SEL-Net: A Self-Supervised Learning-Based Network for PolSAR Image Runway Region Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Ping","family":"Han","sequence":"first","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5796-4156","authenticated-orcid":false,"given":"Yanwen","family":"Peng","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5783-6541","authenticated-orcid":false,"given":"Zheng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Engineering Techniques Training Center, Civil Aviation University of China, Tianjin 300300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1778-8645","authenticated-orcid":false,"given":"Dayu","family":"Liao","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5928-064X","authenticated-orcid":false,"given":"Binbin","family":"Han","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"ref_1","first-page":"233","article-title":"Talking about the Importance and Significance of General Aviation Airport Construction","volume":"15","author":"Zhang","year":"2017","journal-title":"Sci. Technol. Ind. Parks"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1109\/LGRS.2019.2951761","article-title":"PolSAR Image Classification Based on Object-Based Markov Random Field with Polarimetric Auxiliary Label Field","volume":"17","author":"Xu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1109\/TGRS.2016.2539155","article-title":"SAR Image Segmentation Based on Hierarchical Visual Semantic and Adaptive Neighborhood Multinomial Latent Model","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cui, Y., Liu, F., Liu, X., Li, L., and Qian, X. (2022). TCSPANET: Two-Staged Contrastive Learning and Sub-Patch Attention Based Network for Polsar Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14102451"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1109\/JSTARS.2020.3034609","article-title":"Validation of Global Airport Spatial Locations from Open Databases Using Deep Learning for Runway Detection","volume":"14","author":"Ji","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/JSTARS.2020.3036052","article-title":"Airport Detection in SAR Images via Salient Line Segment Detector and Edge-Oriented Region Growing","volume":"14","author":"Tu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/LGRS.2018.2792421","article-title":"Airport Detection in Large-Scale SAR Images via Line Segment Grouping and Saliency Analysis","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"43","article-title":"Airport Runway Detection Algorithm in Remote Sensing Images","volume":"24","author":"Ai","year":"2017","journal-title":"Electron. Opt. Control."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Marapareddy, R., and Pothuraju, A. (2017, January 19\u201321). Runway Detection Using Unsupervised Classification. Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON.2017.8249048"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.11834\/jrs.20198384","article-title":"Fast Detection of Airport Runway Areas in PolSAR Images Using Adaptive Unsupervised Classification","volume":"23","author":"Lu","year":"2019","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_12","first-page":"2084","article-title":"Airport Runway Area Detection in PolSAR Image Combined with Image Segmentation and Classification","volume":"37","author":"Han","year":"2021","journal-title":"J. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"49160","DOI":"10.1109\/ACCESS.2020.2979737","article-title":"A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9820","DOI":"10.1109\/TGRS.2019.2929598","article-title":"Multi-Layer Abstraction Saliency for Airport Detection in SAR Images","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"32118","DOI":"10.1109\/ACCESS.2019.2901776","article-title":"Subjective Saliency Model Driven by Multi-Cues Stimulus for Airport Detection","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","first-page":"99","article-title":"A Survey of SAR Image Segmentation Methods","volume":"38","author":"Zhang","year":"2017","journal-title":"J. Ordnance Equip. Eng."},{"key":"ref_17","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2022, January 18\u201322). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI, Singapore."},{"key":"ref_18","unstructured":"Han, P., and Liang, Y. (2022, January 25\u201327). Airport Runway Area Segmentation in PolSAR Image Based D-Unet Network. Proceedings of the International Workshop on ATM\/CNS, Tokyo, Japan."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018, January 20). Unet++: A Nested u-Net Architecture for Medical Image Segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_20","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. Arxiv Prepr."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Usami, N., Muhuri, A., Bhattacharya, A., and Hirose, A. (2016, January 10\u201315). Proposal of Wet Snowmapping with Focus on Incident Angle Influential to Depolarization of Surface Scattering. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729394"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5693","DOI":"10.1109\/TGRS.2013.2291940","article-title":"Quaternion Neural-Network-Based PolSAR Land Classification in Poincare-Sphere-Parameter Space","volume":"52","author":"Shang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"173627","DOI":"10.1109\/ACCESS.2020.3024546","article-title":"Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery","volume":"8","author":"Tan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102395","DOI":"10.1016\/j.media.2022.102395","article-title":"Boundary-Aware Context Neural Network for Medical Image Segmentation","volume":"78","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_25","first-page":"1","article-title":"Change detection of open-pit mining area based on FM-UNet++ and Gaofen-2 satellite images","volume":"51","author":"Du","year":"2023","journal-title":"Coal Geol. Explor."},{"key":"ref_26","first-page":"283","article-title":"Object-level change detection in multi-source optical remote sensing images combined with UNet++ and multi-level difference modules","volume":"52","author":"Wang","year":"2023","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhong, R., Li, Q., and Zhang, F. (2022). TransUNet++ SAR: Change Detection with Deep Learning about Architectural Ensemble in SAR Images. Remote Sens., 15.","DOI":"10.3390\/rs15010006"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large Kernel Matters\u2013Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., Peng, C., Xue, X., and Sun, J. (2018, January 8\u201314). Exfuse: Enhancing Feature Fusion for Semantic Segmentation. Proceedings of the Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_17"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-Net: Context Encoder Network for 2d Medical Image Segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, Q., Ma, Z., Na, H.E., and Duan, W. (2023). DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation. Comput. Biol. Med., 154.","DOI":"10.1016\/j.compbiomed.2023.106626"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 26). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, P., Liu, Y., and Cheng, Z. (2021, January 22). Airport Runway Detection Based on a Combination of Complex Convolution and ResNet for PolSAR Images. Proceedings of the 2021 SAR in Big Data Era (BIGSARDATA), Nanjing, China.","DOI":"10.1109\/BIGSARDATA53212.2021.9574366"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 14). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_35","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_36","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 12). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the International Conference on Machine Learning (PMLR), Vienna, Austria."},{"key":"ref_37","first-page":"15","article-title":"A Survey of Deep Contrastive Learning","volume":"49","author":"Zhang","year":"2023","journal-title":"Acta Autom. Sin."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16). Dual Attention Network for Scene Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","article-title":"Cgnet: A Light-Weight Context Guided Network for Semantic Segmentation","volume":"30","author":"Wu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 18). Learning a Discriminative Feature Network for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00199"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (2019, January 27). Gated-Scnn: Gated Shape Cnns for Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00533"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lee, J.-S., and Pottier, E. (2017). Polarimetric Radar Imaging: From Basics to Applications, CRC Press.","DOI":"10.1201\/9781420054989"},{"key":"ref_43","first-page":"195","article-title":"Stokes Matrix Parameters and Their Interpretation in Terms of Physical Target Properties","volume":"Volume 1317","author":"Huynen","year":"1990","journal-title":"Proceedings of the Polarimetry: Radar, Infrared, Visible, Ultraviolet, and X-ray"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1126\/science.abh4455","article-title":"A Massive Rock and Ice Avalanche Caused the 2021 Disaster at Chamoli, Indian Himalaya","volume":"373","author":"Shugar","year":"2021","journal-title":"Science"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4708\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:58:28Z","timestamp":1760129908000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":44,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194708"],"URL":"https:\/\/doi.org\/10.3390\/rs15194708","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]}}}