{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:29:37Z","timestamp":1765232977277,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,26]],"date-time":"2020-09-26T00:00:00Z","timestamp":1601078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jidong Wu","award":["2018YFC1508903"],"award-info":[{"award-number":["2018YFC1508903"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the complexity of airport background and runway structure, the performances of most runway extraction methods are limited. Furthermore, at present, the military fields attach greater importance to semantic changes of some objects in the airport, but few studies have been done on this subject. To address these issues, this paper proposes an accurate runway change analysis method, which comprises two stages: airport runway extraction and runway change analysis. For the former stage, some airport knowledge, such as chevron markings and runway edge markings, are first applied in combination with multiple features of runways to improve the accuracy. In addition, the proposed method can accomplish airport runway extraction automatically. For the latter, semantic information and vector results of runway changes can be obtained simultaneously by comparing bi-temporal runway extraction results. In six test images with about 0.5-m spatial resolution, the average completeness of runway extraction is nearly 100%, and the average quality is nearly 89%. In addition, the final experiment using two sets of bi-temporal very high-resolution (VHR) images of runway changes demonstrated that semantic results obtained by our method are consistent with the real situation and the final accuracy is over 80%. Overall, the airport knowledge, especially chevron markings for runways and runway edge markings, are critical to runway recognition\/detection, and multiple features of runways, such as shape and parallel line features, can further improve the completeness and accuracy of runway extraction. Finally, a small step has been taken in the study of runway semantic changes, which cannot be accomplished by change detection alone.<\/jats:p>","DOI":"10.3390\/rs12193163","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:02:58Z","timestamp":1601280178000},"page":"3163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Airport Knowledge-Based Method for Accurate Change Analysis of Airport Runways in VHR Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9866-8073","authenticated-orcid":false,"given":"Wei","family":"Ding","sequence":"first","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8208-8373","authenticated-orcid":false,"given":"Jidong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,26]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Zeng, F., Cheng, L., Li, N., Xia, N., Ma, L., Zhou, X., and Li, M. (2019). A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11192204"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s11571-012-9223-z","article-title":"Airport Detection in Remote Sensing Images: A Method Based on Saliency Map","volume":"7","author":"Wang","year":"2012","journal-title":"Cogn. Neurodyn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1109\/JSTARS.2017.2669335","article-title":"Multiresolution Airport Detection via Hierarchical Reinforcement Learning Saliency Model","volume":"10","author":"Zhao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/JSTARS.2016.2620900","article-title":"Airport Detection and Aircraft Recognition Based on Two-Layer Saliency Model in High Spatial Resolution Remote-Sensing Images","volume":"10","author":"Zhang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"2511","article-title":"Airport Detection Based on Superpixel Segmentation and Saliency Analysis for Remote Sensing Images","volume":"2018","author":"Wang","year":"2018","journal-title":"Int. Geosci. Remote Sens. Symp. (IGARSS)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/LGRS.2018.2828502","article-title":"Airport Extraction via Complementary Saliency Analysis and Saliency-Oriented Active Contour Model","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Chen, F., Ren, R., Van De Voorde, T., Xu, W., Zhou, G., and Zhou, Y. (2018). Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks. Remote Sens., 10.","DOI":"10.3390\/rs10030443"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhu, M., Li, S., Feng, H., Ma, S., and Che, J. (2018). End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks. Remote Sens., 10.","DOI":"10.3390\/rs10101516"},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.rse.2007.10.002","article-title":"Using Local Transition Probability Models in Markov Random Fields for Forest Change Detection","volume":"112","author":"Liu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Seo, D.K., Kim, Y., Eo, Y., Park, W., and Park, H. (2017). Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection. Remote Sens., 9.","DOI":"10.3390\/rs9111163"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4738","DOI":"10.1109\/JSTARS.2014.2298332","article-title":"Semiautomatic Airport Runway Extraction Using a Line-Finder-Aided Level Set Evolution","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LGRS.2012.2210189","article-title":"Texture-Based Airport Runway Detection","volume":"10","author":"Aytekin","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1109\/LGRS.2014.2299898","article-title":"Recognition of Airport Runways in FLIR Images Based on Knowledge","volume":"11","author":"Wu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, C., Cheng, I., and Basu, A. (2018). Real-Time Runway Detection for Infrared Aerial Image Using Synthetic Vision and an ROI Based Level Set Method. Remote Sens., 10.","DOI":"10.3390\/rs10101544"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lv, Z., Shi, W., Zhou, X., and Benediktsson, J.A. (2017). Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9111112"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lv, Z., Liu, T., Wan, Y., Benediktsson, J.A., and Zhang, X. (2018). Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10030472"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1109\/JSTARS.2016.2514610","article-title":"Change Detection Based on Conditional Random Field with Region Connection Constraints in High-Resolution Remote Sensing Images","volume":"9","author":"Zhou","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/LGRS.2013.2252879","article-title":"Unsupervised Change Detection with Expectation-Maximization-Based Level Set","volume":"11","author":"Hao","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","first-page":"341","article-title":"Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest","volume":"42","author":"Feng","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2005.09.008","article-title":"A Change Detection Model Based on Neighborhood Correlation Image Analysis and Decision Tree Classification","volume":"99","author":"Im","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/TNNLS.2019.2910571","article-title":"Bipartite Differential Neural Network for Unsupervised Image Change Detection","volume":"31","author":"Liu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TGRS.2019.2948659","article-title":"From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques","volume":"58","author":"Hou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","unstructured":"U.S. Department of Transportation. Federal Aviation Administration (2020, July 20). \u201cAirport Design.\u201d Aviation 1 (AC 150\/5300-13A): 1\u2013322, Available online: https:\/\/www.faa.gov\/documentLibrary\/media\/Advisory_Circular\/150-5300-13A-chg1-interactive-201907.pdf."},{"key":"ref_29","unstructured":"U.S. Department of Transportation. Federal Aviation Administration (2020, July 20). \u201cStandards for Airport Markings\u201d. (AC 150\/5340-1M): 1\u2013171, Available online: https:\/\/www.faa.gov\/documentLibrary\/media\/Advisory_Circular\/150-5340-1M.pdf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201326). Frequency-Tuned Salient Region Detection. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, Miami Beach, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean Shift: A Robust Approach toward Feature Space Analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/cviu.1999.0831","article-title":"Robust Detection of Lines Using the Progressive Probabilistic Hough Transform","volume":"78","author":"Matas","year":"2000","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan, H., Guo, X., Ma, C., and Xu, C. (2020, January 16\u201318). Dynamic Refinement Network for Oriented and Densely Packed Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01122"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1109\/JSTARS.2015.2458582","article-title":"Building Damage Detection Using Object-Based Image Analysis and ANFIS from High-Resolution Image (Case Study: BAM Earthquake, Iran)","volume":"9","author":"Janalipour","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:14:02Z","timestamp":1760177642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,26]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193163"],"URL":"https:\/\/doi.org\/10.3390\/rs12193163","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,9,26]]}}}