{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:09:48Z","timestamp":1773155388011,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"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":["41571337"],"award-info":[{"award-number":["41571337"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: 50\u00a0km2); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: 113\u00a0km2); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: 5\u00a0km2). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.<\/jats:p>","DOI":"10.3390\/rs13061146","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T11:48:22Z","timestamp":1615981702000},"page":"1146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3167-0974","authenticated-orcid":false,"given":"Yuliang","family":"Nie","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1020-064X","authenticated-orcid":false,"given":"Qiming","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"given":"Haizhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Air Force Research Institute, Beijing 100085, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhai, W., Huang, C.L., and Pei, W.S. (2019). Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image. Remote Sens., 11.","DOI":"10.3390\/rs11080897"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1109\/JSTARS.2018.2822825","article-title":"Earthquake\/Tsunami Damage Level Mapping of Urban Areas Using Full Polarimetric SAR Data","volume":"11","author":"Ji","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1109\/JSTARS.2018.2818939","article-title":"Urban Damage Level Mapping Based on Co-Polarization Coherence Pattern Using Multitemporal Polarimetric SAR Data","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ji, Y.Q., Sri Sumantyo, J.T.S., Chua, M.Y., and Waqar, M.M. (2018). Earthquake\/Tsunami Damage Assessment for Urban Areas Using Post-Event PolSAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10071088"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Ueda, N., Al-Najjar, H.A.H., and Halin, A.A. (2020). Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images. Remote Sens., 12.","DOI":"10.3390\/rs12213529"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.5194\/nhess-18-65-2018","article-title":"Detection of Collapsed Buildings from Lidar Data Due to the 2016 Kumamoto Earthquake in Japan","volume":"18","author":"Moya","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_7","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":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.14358\/PERS.77.10.1025","article-title":"Damage Assessment of 2010 Haiti Earthquake with Post-Earthquake Satellite Image by Support Vector Selection and Adaptation","volume":"77","author":"Kaya","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nex, F., Duarte, D., Tonolo, F.G., and Kerle, N. (2019). Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions. Remote Sens., 11.","DOI":"10.3390\/rs11232765"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/22797254.2018.1458584","article-title":"Contextual Classification Using Photometry and Elevation Data for Damage Detection After an Earthquake Event","volume":"51","author":"Rupnik","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nex, F., Duarte, D., Steenbeek, A., and Kerle, N. (2019). Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions. Remote Sens., 11.","DOI":"10.3390\/rs11030287"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ge, P.L., Gokon, H., and Meguro, K. (2020). A Review on Synthetic Aperture Radar-based Building Damage Assessment in Disasters. Remote Sens. Environ., 240.","DOI":"10.1016\/j.rse.2020.111693"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1193\/1.1774182","article-title":"Use of Satellite SAR Intensity Imagery for Detecting Building Areas Damaged Due to Earthquakes","volume":"20","author":"Matsuoka","year":"2004","journal-title":"Earthq. Spectra"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.3390\/rs2092111","article-title":"Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery","volume":"2","author":"Matsuoka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"241","DOI":"10.20965\/jdr.2017.p0241","article-title":"Extraction of Collapsed Buildings in the 2016 Kumamoto Earthquake Using Multi-Temporal PALSAR-2 Data","volume":"12","author":"Liu","year":"2017","journal-title":"J. Disaster Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1080\/01431160600928567","article-title":"Mapping Damage During the Bam (Iran) Earthquake Using Interferometric Coherence","volume":"28","author":"Hoffmann","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/TGRS.2012.2210050","article-title":"Tsunami Damage Investigation of Built-Up Areas Using Multitemporal Spaceborne Full Polarimetric SAR Images","volume":"51","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1109\/JPROC.2012.2195469","article-title":"Disaster Monitoring by Fully Polarimetric SAR Data Acquired With ALOS-PALSAR","volume":"100","author":"Yamaguchi","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Park, S.-E., and Jung, Y.T. (2020). Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12010137"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1109\/TGRS.2006.883149","article-title":"Coherence- and Amplitude-based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data","volume":"45","author":"Arciniegas","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18","DOI":"10.25103\/jestr.113.03","article-title":"Urban Change Detection in TerraSAR Image Using the Difference Method and SAR Coherence Coefficient","volume":"11","author":"Zhang","year":"2018","journal-title":"J. Eng. Sci. Technol. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"646","DOI":"10.20965\/jdr.2017.p0646","article-title":"Machine Learning Based Building Damage Mapping from the ALOS-2\/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake","volume":"12","author":"Bai","year":"2017","journal-title":"J. Disaster Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Natsuaki, R., Nagai, H., Tomii, N., and Tadono, T. (2018). Sensitivity and Limitation in Damage Detection for Individual Buildings Using InSAR Coherence\u2014A Case Study in 2016 Kumamoto Earthquakes. Remote Sens., 10.","DOI":"10.3390\/rs10020245"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"456","DOI":"10.20965\/jdr.2019.p0456","article-title":"Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval","volume":"14","author":"Ge","year":"2019","journal-title":"J. Disaster Res."},{"key":"ref_25","unstructured":"Saha, S., Bovolo, F., and Bruzzone, L. (2020). Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding. IEEE Trans. Geosci. Remote Sens., 1\u201313."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"S183","DOI":"10.1193\/1.4000120","article-title":"Extraction of Tsunami-flooded Areas and Damaged Buildings in the 2011 Tohoku-oki Earthquake from TerraSAR-X Intensity Images","volume":"29","author":"Liu","year":"2013","journal-title":"Earthq. Spectra"},{"key":"ref_27","unstructured":"Benediktsson, J.A., Bovolo, F., Bruzzone, L., Bruzzone, L., Bovolo, F., and Saha, S. (2018, January 10\u201312). Destroyed-buildings Detection from VHR SAR Images Using Deep Features. Proceedings of the Image and Signal Processing for Remote Sensing XXIV, Berlin, Germany."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guo, H.D. (2009). Study of Detecting Method with Advanced Airborne and Spaceborne Synthetic Aperture Radar Data for Collapsed Urban Buildings from the Wenchuan Earthquake. J. Appl. Remote Sens., 3.","DOI":"10.1117\/1.3153902"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/LGRS.2011.2178392","article-title":"A New Approach to Collapsed Building Extraction Using RADARSAT-2 Polarimetric SAR Imagery","volume":"9","author":"Li","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8952","DOI":"10.1080\/01431161.2013.860566","article-title":"Damage Assessment in Urban Areas Using Post-Earthquake Airborne PolSAR Imagery","volume":"34","author":"Zhao","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/LGRS.2015.2443018","article-title":"Building Collapse Assessment by the Use of Postearthquake Chinese VHR Airborne SAR","volume":"12","author":"Shi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3792","DOI":"10.1109\/JSTARS.2016.2580610","article-title":"Building Collapse Assessment in Urban Areas Using Texture Information from Postevent SAR Data","volume":"9","author":"Sun","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"S185","DOI":"10.1193\/121516eqs232m","article-title":"Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post-Event Dual Polarization Synthetic Aperture Radar Imagery","volume":"33","author":"Bai","year":"2017","journal-title":"Earthq. Spectra"},{"key":"ref_34","first-page":"955","article-title":"Buildings Damage Assessment Using Texture Features of Polarization Decomposition Components","volume":"21","author":"Chen","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.cageo.2018.01.018","article-title":"Building Damage Assessment from PolSAR Data Using Texture Parameters of Statistical Model","volume":"113","author":"Li","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1250006-1","DOI":"10.1142\/S0578563412500064","article-title":"Mapping of Building Damage of the 2011 Tohoku Earthquake Tsunami in Miyagi Prefecture","volume":"54","author":"Gokon","year":"2012","journal-title":"Coast. Eng. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3130\/aijs.64.65_5","article-title":"Classifications of Structural Types and Damage Patterns of Buildings for Earthquake Field Investigation","volume":"64","author":"Okada","year":"1999","journal-title":"J. Struct. Constr. Eng. (Trans. AIJ)"},{"key":"ref_38","unstructured":"(2019, September 30). Quick Report of the Field Survey on the Building Damage by the 2016 Kumamoto Earthquake, Technical Note No. 929, (In Japanese)."},{"key":"ref_39","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_40","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Image: From Basics to Applications, CRC Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tong, S.W., Liu, X.G., Chen, Q.H., Zhang, Z.J., and Xie, G.Q. (2019). Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter. Remote Sens., 11.","DOI":"10.3390\/rs11040451"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Van Zyl, J.J. (1992). Application of Cloude\u2019s Target Decomposition Theorem to Polarimetric Imaging Radar Data. Radar Polarimetry, SPIE.","DOI":"10.1117\/12.140615"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A Review of Target Decomposition Theorems in Radar Polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1364\/JOSAA.23.003036","article-title":"Shannon Entropy of Partially Polarized and Partially Coherent Light with Gaussian Fluctuations","volume":"23","author":"Refregier","year":"2006","journal-title":"J. Opt. Soc. Am. A Opt. Image Sci. Vis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1109\/TGRS.2008.926115","article-title":"Information Theory-based Approach for Contrast Analysis in Polarimetric and\/or Interferometric SAR Images","volume":"46","author":"Morio","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.isprsjprs.2017.10.004","article-title":"A Hybrid Training Approach for Leaf Area Index Estimation via Cubist and Random Forests Machine-learning","volume":"135","author":"Houborg","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/LGRS.2004.830127","article-title":"Generalized Optimization of Polarimetric Contrast Enhancement","volume":"1","author":"Yang","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TAP.1979.1142099","article-title":"Optimum Antenna Polarizations for Target Discrimination in Clutter","volume":"27","author":"Ioannidis","year":"1979","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TAP.1987.1144209","article-title":"On the Polarimetric Contrast Optimization","volume":"35","author":"Kostinski","year":"1987","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"15252","DOI":"10.1029\/JB093iB12p15252","article-title":"Optimal Polarizations for Achieving Maximum Contrast in Radar Images","volume":"93","author":"Swartz","year":"1988","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mott, H., and Boerner, W.M. (1997). Polarimetric Contrast Enhancement Coefficients for Perfecting High-resolution POL-SAR\/SAL Image Feature Extraction. Wideband Interferometric Sensing and Imaging Polarimetry, SPIE.","DOI":"10.1117\/12.300636"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/36.841978","article-title":"Numerical Methods for Solving the Optimal Problem of Contrast Enhancement","volume":"38","author":"Yang","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/LGRS.2014.2381211","article-title":"The Use of a Modified GOPCE Method for Forest and Nonforest Discrimination","volume":"12","author":"Yin","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","unstructured":"Yang, J., and Cui, Y. (2009, January 20\u201322). A Novel Method for Ship Detection in Polarimetric SAR Images Using GOPCE. Proceedings of the IET International Radar Conference 2009, Guilin, China."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, H.Z., Wang, Q., Zeng, Q.M., and Jiao, J. (2015, January 26\u201331). A Novel Approach to Building Collapse Detection from Post-seismic Polarimetric SAR Imagery by Using Optimization of Polarimetric Contrast Enhancement. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS 2015), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326516"},{"key":"ref_57","first-page":"6859","article-title":"The Jeffries\u2013Matusita Distance for the Case of Complex Wishart Distribution As a Separability Criterion for Fully Polarimetric SAR Data","volume":"35","author":"Dabboor","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Haralick, R.M., Shanmugam, K., and Dinstein, I. (1973). Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern., 610\u2013621.","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gong, L., Wang, C., Wu, F., Zhang, J., Zhang, H., and Li, Q. (2016). Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8110887"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1109\/LGRS.2016.2604841","article-title":"Signature Analysis of Building Damage with TerraSAR-X New Staring SpotLight Mode Data","volume":"13","author":"Wu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box-A Toolbox and Application Programming Interface for EnMAP Data Processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1146\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:37:08Z","timestamp":1760161028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1146"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,17]]},"references-count":61,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061146"],"URL":"https:\/\/doi.org\/10.3390\/rs13061146","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,17]]}}}