{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:32:47Z","timestamp":1768833167839,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"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":["61871175"],"award-info":[{"award-number":["61871175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"College Key Research Project of Henan Province","award":["19A420005"],"award-info":[{"award-number":["19A420005"]}]},{"name":"College Key Research Project of Henan Province","award":["21A520004"],"award-info":[{"award-number":["21A520004"]}]},{"name":"Plan of Science and Technology of Henan Province","award":["212102210093"],"award-info":[{"award-number":["212102210093"]}]},{"name":"Plan of Science and Technology of Henan Province","award":["202102210175"],"award-info":[{"award-number":["202102210175"]}]},{"name":"Plan of Science and Technology of Henan Province","award":["212102210101"],"award-info":[{"award-number":["212102210101"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application","award":["KLSMNR-202102"],"award-info":[{"award-number":["KLSMNR-202102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology.<\/jats:p>","DOI":"10.3390\/rs13234882","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T02:56:14Z","timestamp":1638413774000},"page":"4882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Method for Layover Detection in Mountainous Areas with SAR Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2211-2965","authenticated-orcid":false,"given":"Lin","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475000, China"},{"name":"Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}]},{"given":"Hongxia","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475000, China"},{"name":"Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}]},{"given":"Yuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475000, China"},{"name":"Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"School of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu 476000, China"}]},{"given":"Zhengwei","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475000, China"},{"name":"Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4358-6449","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475000, China"},{"name":"Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2019). High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11101206"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1007\/s40333-017-0104-5","article-title":"Evaluating land subsidence by field survey and D-InSAR technique in Damaneh City, Iran","volume":"9","author":"Ghazifard","year":"2017","journal-title":"J. Arid. Land"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kang, M., and Baek, J. (2021). SAR Image Change Detection via Multiple-Window Processing with Structural Similarity. Sensors, 21.","DOI":"10.3390\/s21196645"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4517","DOI":"10.1109\/JSTARS.2019.2953128","article-title":"Change Detection From Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network","volume":"12","author":"Gao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liu, G., Zhang, C., Atkinson, P.M., Tan, X., Jian, X., Zhou, X., and Li, Y. (2020). Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030548"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TGRS.2014.2349575","article-title":"An automatic u-distribution and markov random field segmentation algorithm for PolSAR images","volume":"53","author":"Doulgeris","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1109\/JSTARS.2013.2259219","article-title":"Radarsat-2 Polarimetric SAR Data for Boreal Forest Classification Using SVM and a Wrapper Feature Selector","volume":"6","author":"Maghsoudi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.3390\/s8063819","article-title":"An Assessment of the Altimetric Information Derived from Spaceborne SAR (RADARSAT-1, SRTM3) and Optical (ASTER) Data for Cartographic Application in the Amazon Region","volume":"8","author":"Paradella","year":"2008","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"18092","DOI":"10.1073\/pnas.1307965110","article-title":"Earth-viewing satellite perspectives on the Chelyabinsk meteor event","volume":"110","author":"Miller","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3724\/SP.J.1146.2009.00283","article-title":"A Large Scene Imaging Algorithm for Missile-borne Side-looking SAR","volume":"32","author":"Yi","year":"2010","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/36.298013","article-title":"The wavenumber shift in SAR interferometry","volume":"32","author":"Gatelli","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/TAES.2002.1145755","article-title":"Layover solution in multibaseline SAR interferometry","volume":"38","author":"Gini","year":"2002","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/TGRS.2004.838389","article-title":"A maximum-likelihood estimator to simultaneously unwrap, geocode, and fuse SAR interferograms from different viewing geometries into one digital elevation model","volume":"43","author":"Eineder","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"961","article-title":"Layover and Shadow Detection Based on Distributed Spaceborne Single-Baseline InSAR","volume":"26","author":"Cai","year":"2010","journal-title":"J. Signal Process."},{"key":"ref_16","first-page":"396","article-title":"A method for layover and shadow detecting in InSAR","volume":"44","author":"Ren","year":"2013","journal-title":"J. Cent. South Univ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Du, X.Y., Yang, Q., Cai, B., and Liang, D.N. (2017, January 16\u201319). A new method on shadow and layover detection of InSAR. Proceedings of the IEEE 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), Xi\u2019an, China.","DOI":"10.1109\/APCAP.2017.8420751"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1080\/07038992.1992.10855137","article-title":"Analytic Formulation of Spaceborne SAR Image Geocoding and \u201cValue-Added\u201d Product Generation Procedures using Digital Elevation Data","volume":"18","author":"Guindon","year":"1992","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1109\/LGRS.2014.2306820","article-title":"Polarimetric Response of Landslides at X-Band Following the Wenchuan Earthquake","volume":"11","author":"Li","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kakooei, M., Nascetti, A., and Ban, Y. (2018, January 22\u201327). Sentinel-1 Global Coverage Foreshortening Mask Extraction: An Open Source Implementation Based on Google Earth Engine. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519098"},{"key":"ref_21","first-page":"85","article-title":"Identification of layover and shadows regions in SAR images: Taking Badong as an example","volume":"11","author":"Zhang","year":"2019","journal-title":"Bull. Surv. Mapp."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8367","DOI":"10.1109\/TGRS.2020.3045505","article-title":"Layover Compensation Method for Regional Spaceborne SAR Imagery Without GCPs","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.3724\/SP.J.1006.2020.94134","article-title":"Extraction of crop acreage based on multi-temporal and dual-polarization SAR data","volume":"46","author":"Na","year":"2020","journal-title":"Acta Agron. Sin."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1080\/01431161.2014.919679","article-title":"Polarimetric analysis of multi-temporal RADARSAT-2 SAR images for wheat monitoring and mapping","volume":"35","author":"Xu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Angelliaume, S. (2016). Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications. Remote Sens., 8.","DOI":"10.3390\/rs8050406"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TGRS.2010.2099124","article-title":"Four-Component Scattering Power Decomposition With Rotation of Coherency Matrix","volume":"49","author":"Yamaguchi","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"(2021, March 28). National Earth System Science Data Center. Available online: http:\/\/www.geodata.cn\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1080\/01431161.2018.1563841","article-title":"Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients","volume":"40","author":"Zheng","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"81","DOI":"10.5589\/m07-011","article-title":"Full fuzzy land cover mapping using remote sensing data based on fuzzy c-means and density estimation","volume":"32","author":"Kumar","year":"2007","journal-title":"Can. J. Remote Sens."},{"key":"ref_30","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."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/LGRS.2019.2945546","article-title":"Semisupervised Classification Based on SLIC Segmentation for Hyperspectral Image","volume":"17","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","first-page":"3817","article-title":"Image segmentation based on SLIC and conditional random field","volume":"32","author":"Sun","year":"2015","journal-title":"Appl. Res. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3808\/jei.201500296","article-title":"Water quality characteristics and integrated assessment based on multistep correlation analysis in the Danjiangkou reservoir, China","volume":"25","author":"Tan","year":"2015","journal-title":"J. Environ. Inf."},{"key":"ref_34","first-page":"44","article-title":"Risk analysis of synchronous asynchronous encounter probability of rich-poor precipitation in the Middle Route of South-to-North Water","volume":"22","author":"Kang","year":"2011","journal-title":"J. Adv. Water Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"767","DOI":"10.3390\/rs10050767","article-title":"Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China","volume":"10","author":"Wu","year":"2018","journal-title":"Remote Sens."},{"key":"ref_36","first-page":"8886","article-title":"Ecological protection and restoration of mountains-rivers-vegetations-farmlands-lakes-grasslands in Nantaihang area, Henan Province: Integrated landscape management","volume":"39","author":"Yu","year":"2019","journal-title":"Acta Ecol. Sin."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Geudtner, D., Torres, R., Snoeij, P., Ostergaard, A., and Navas-Traver, I. (May, January 29). Sentinel-1 mission capabilities and SAR system. Proceedings of the 2013 IEEE Radar Conference, Ottawa, ON, Canada.","DOI":"10.1109\/RADAR.2013.6586141"},{"key":"ref_38","first-page":"35","article-title":"Polarimetric SAR ship detection based on polarimetric rotation domain features and superpixel technique","volume":"10","author":"Cui","year":"2021","journal-title":"J. Radars"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4882\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:38:28Z","timestamp":1760168308000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,1]]},"references-count":38,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234882"],"URL":"https:\/\/doi.org\/10.3390\/rs13234882","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,1]]}}}