{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T09:26:44Z","timestamp":1777109204691,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"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","doi-asserted-by":"publisher","award":["41201468\uff1b41701536, 61701047, 41674040"],"award-info":[{"award-number":["41201468\uff1b41701536, 61701047, 41674040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of\u00a0Hunan Province","doi-asserted-by":"publisher","award":["2017JJ3322;2019JJ50639"],"award-info":[{"award-number":["2017JJ3322;2019JJ50639"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014472","name":"Scientific Research Foundation of Hunan Provincial Education Department","doi-asserted-by":"publisher","award":["16B004;16C0043"],"award-info":[{"award-number":["16B004;16C0043"]}],"id":[{"id":"10.13039\/100014472","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 \u00d7 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.<\/jats:p>","DOI":"10.3390\/rs12030560","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T11:50:28Z","timestamp":1581076228000},"page":"560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2432-9583","authenticated-orcid":false,"given":"Lifu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouhao","family":"Pan","sequence":"additional","affiliation":[{"name":"China Academy of Electronics and Information Technology, Beijing 100041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-3414","authenticated-orcid":false,"given":"Jin","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7100-826X","authenticated-orcid":false,"given":"Zhihui","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-4899","authenticated-orcid":false,"given":"Xuemin","family":"Xing","sequence":"additional","affiliation":[{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"},{"name":"School of Traffic &amp; Transportation Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"ref_1","first-page":"156","article-title":"InSAR Principles-Guidelines for SAR Interferometry Processing and Interpretation","volume":"19","author":"Ferretti","year":"2007","journal-title":"J. Financ. Stabil."},{"key":"ref_2","first-page":"1112","article-title":"A Fast Method of Airport Detection in Large 2 scale SAR Image with High Resolution","volume":"15","author":"Zhang","year":"2010","journal-title":"J. Image Gr."},{"key":"ref_3","first-page":"1714","article-title":"Airport runway detection algorithm based on local multi-features","volume":"35","author":"Yan","year":"2014","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_4","first-page":"375","article-title":"Airport detection based on near parallelity of line segments and GBVS saliency","volume":"34","author":"Zhu","year":"2015","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_5","unstructured":"Kou, Z., Shi, Z., and Liu, L. (2012, January 16). Airport detection based on Line Segment Detector. Proceedings of the International Conference on Computer Vision in Remote Sensing, Xiamen, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xiong, W., Zhong, J., and Zhou, Y. (2012, January 27\u201330). Automatic recognition of airfield runways based on Radon transform and hypothesis testing in SAR images. Proceedings of the Global Symposium on Millimeter-Waves, Harbin, China.","DOI":"10.1109\/GSMM.2012.6314098"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/LGRS.2014.2384051","article-title":"Airport Target Detection in Remote Sensing Images: A New Method Based on Two-Way Saliency","volume":"12","author":"Zhu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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 Geosci. Remote Sens."},{"key":"ref_9","first-page":"30","article-title":"A method of Airport Extraction Based on Template searching from High Resolution SAR Image","volume":"2","author":"Zhang","year":"2010","journal-title":"Remote Sens. Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_11","first-page":"1300","article-title":"Convolutional Neural Networks in Image Understanding","volume":"42","author":"Chang","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_12","unstructured":"Jonathan, L., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_13","first-page":"3436","article-title":"SAR image scece classification with fully convolutional network and modified conditional random field-recurrent neural network","volume":"36","author":"Tang","year":"2016","journal-title":"J. Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","unstructured":"Chen, L.C., and George, P. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_16","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, G., Anton, M., and Shen, C. (2017, January 21\u201326). RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., and Yu, G. (2017, January 21\u201326). Large Kernel Matters\u2014Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., and Peng, C. (2018, January 8\u201314). ExFuse: Enhancing Feature Fusion for Semantic Segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_17"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, H., Shi, J., and Qi, X. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_21","unstructured":"Liang-Chieh, C., and Papandreou, G. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, L., and George, P. (2018, January 8\u201314). Encoder-Decoder with Atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, C., Chen, L.C., and Schroff, F. (2019, January 16\u201320). Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref_24","first-page":"44","article-title":"Airport detection using convolutional neural network and salient feature","volume":"7","author":"Yu","year":"2019","journal-title":"Bull. Surv. Mapp."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/LGRS.2017.2712638","article-title":"Airport Detection Based on a Multiscale Fusion Feature for Optical Remote Sensing Images","volume":"14","author":"Xiao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zeng, F., and Cheng, L. (2019). A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11192204"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1109\/LGRS.2019.2904076","article-title":"Remote Sensing Airport Detection Based on End-to-End Deep Transferable Convolutional Neural Networks","volume":"16","author":"Li","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","unstructured":"He, K., Zhang, X., and Ren, S. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., and Ren, S. (2016, January 11\u201314). Identity Mappings in Deep Residual Networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Laurens, V.D.M. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, P., and Chen, L. (2019). Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network. Sensors, 19.","DOI":"10.3390\/s19163576"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, L., and Cui, X. (2019). A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration. Sensors, 19.","DOI":"10.3390\/s19112479"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., and Tian, H. (2019, January 16\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., and Orts-Escolano, S. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/560\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:47Z","timestamp":1760172947000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,7]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030560"],"URL":"https:\/\/doi.org\/10.3390\/rs12030560","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,7]]}}}