{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:16:06Z","timestamp":1772792166026,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T00:00:00Z","timestamp":1635033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japanese Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KA-KENHI)","award":["20H02411, 19H02408"],"award-info":[{"award-number":["20H02411, 19H02408"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies.<\/jats:p>","DOI":"10.3390\/rs13214272","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"4272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7904-6903","authenticated-orcid":false,"given":"Davoud","family":"Omarzadeh","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5645-0188","authenticated-orcid":false,"given":"Sadra","family":"Karimzadeh","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"},{"name":"Remote Sensing Laboratory, University of Tabriz, Tabriz 5166616471, Iran"},{"name":"Department of Architecture and Building Engineering, Tokyo Institute of Technology, 4259-G3-2 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3061-5754","authenticated-orcid":false,"given":"Masashi","family":"Matsuoka","sequence":"additional","affiliation":[{"name":"Department of Architecture and Building Engineering, Tokyo Institute of Technology, 4259-G3-2 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan"}]},{"given":"Bakhtiar","family":"Feizizadeh","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"},{"name":"Remote Sensing Laboratory, University of Tabriz, Tabriz 5166616471, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ijdrr.2017.02.016","article-title":"From a GIS-based hybrid site condition map to an earthquake damage as-sessment in Iran: Methods and trends","volume":"22","author":"Karimzadeh","year":"2017","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Firuzi, E., Ansari, A., Hosseini, K.A., and Karkooti, E. (2020). Developing a customized system for generating near real time ground motion ShakeMap of Iran\u2019s earthquakes. J. Earthq. Eng., 1\u201323.","DOI":"10.1080\/13632469.2020.1814450"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/JPROC.2012.2196404","article-title":"Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives","volume":"100","author":"Paolo","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/978-90-481-2238-7_15","article-title":"Utilizing new technologies in managing hazards and disasters","volume":"Volume 2","author":"Eguchi","year":"2009","journal-title":"Geospatial Techniques in Urban Hazard and Disaster Analysis"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ghasemi, M., Karimzadeh, S., Matsuoka, M., and Feizizadeh, B. (2021). What Would Happen If the M 7.3 (1721) and M 7.4 (1780) Historical Earthquakes of Tabriz City (NW Iran) Occurred Again in 2021?. ISPRS Int. J. GeoInf., 10.","DOI":"10.3390\/ijgi10100657"},{"key":"ref_6","first-page":"5989","article-title":"Factors determining human casualty levels in earthquakes: Mortality prediction in building collapse. In Proceedings of the tenth world conference on earthquake engineering","volume":"10","author":"Coburn","year":"1992","journal-title":"Balkema Rotterdam"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jaiswal, K., and Wald, J. (2008). Creating a Global Building Inventory for Earthquake Loss Assessment and Risk Management.","DOI":"10.3133\/ofr20081160"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1080\/07038992.2019.1704622","article-title":"Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran","volume":"45","author":"Feizizadeh","year":"2019","journal-title":"Can. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., Samsonov, S., and Matsuoka, M. (2017, January 23\u201328). Block-based damage assessment of the 2012 Ahar-Varzaghan, Iran, earthquake through SAR remote sensing data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), FortWorth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127264"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Najafi, P., Feizizadeh, B., and Navid, H. (2021). A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13050937"},{"key":"ref_12","unstructured":"United Nations, and International Search and Rescue Advisory Group (2015). INSARAG Guidelines, United Nations International Search and Rescue Advisory Group. Volume II: Preparedness and Response; Manual B: Operations."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, Q., Yin, Q., Zheng, X., and Chen, Z. (2021). Remote sensing image building detection method based on Mask R-CNN. Complex Intell. Syst., 1\u20139.","DOI":"10.1007\/s40747-021-00322-z"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1193\/1.2098629","article-title":"Object-Oriented Image Understanding and Post-Earthquake Damage Assessment for the 2003 Bam, Iran, Earthquake","volume":"21","author":"Gusella","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1193\/1.2101807","article-title":"Visual Damage Interpretation of Buildings in Bam City using QuickBird Images following the 2003 Bam, Iran, Earthquake","volume":"21","author":"Yamazaki","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/JSTARS.2012.2205559","article-title":"Combined Edge Segment Texture Analysis for the Detection of Damaged Buildings in Crisis Areas","volume":"5","author":"Klonus","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2019.01.008","article-title":"3D gray level co-occurrence matrix and its application to identifying collapsed buildings","volume":"149","author":"Moya","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","first-page":"1233","article-title":"Urban building damage detection from VHR imagery by including temporal and spatial texture features","volume":"16","author":"Song","year":"2012","journal-title":"Yaogan Xuebao J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1080\/01431161.2015.1034890","article-title":"Extraction of earthquake-induced collapsed buildings using very high-resolution imagery and airborne lidar data","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TGRS.2008.2002695","article-title":"Exploiting SAR and VHR Optical Images to Quantify Damage Caused by the 2003 Bam Earthquake","volume":"47","author":"Chini","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.14358\/PERS.77.10.1011","article-title":"Building Extraction and Rubble Mapping for City Port-au-Prince Post-2010 Earthquake with GeoEye-1 Imagery and Lidar Data","volume":"77","author":"Ural","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1080\/01431160701241746","article-title":"Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition","volume":"28","author":"Tian","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.scitotenv.2014.04.048","article-title":"OBIA based hierarchical image classification for industrial lake water","volume":"487","author":"Avci","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/JSTARS.2020.3041859","article-title":"Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification","volume":"14","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"025010","DOI":"10.1117\/1.JRS.12.025010","article-title":"Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery","volume":"12","author":"Fu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2013.05.003","article-title":"Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective","volume":"82","author":"Arvor","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1080\/01431161.2013.873151","article-title":"Combining per-pixel and object-based classifications for mapping land cover over large areas","volume":"35","author":"Costa","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1080\/01431160500185227","article-title":"Some issues in the classification of DAIS hyperspectral data","volume":"27","author":"Pal","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2018.1426092","article-title":"Geographic object-based image analysis (GEOBIA): Emerging trends and future op-portunities","volume":"55","author":"Chen","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1080\/17538947.2018.1485753","article-title":"Mapping informal settlement indicators using object-oriented analysis in the Middle East","volume":"12","author":"Fallatah","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105073","DOI":"10.1016\/j.catena.2020.105073","article-title":"An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran","volume":"198","author":"Feizizadeh","year":"2020","journal-title":"CATENA"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ghasemi, M., Karimzadeh, S., and Feizizadeh, B. (2021). Urban classification using preserved information of high dimensional textural features of Sentinel-1 images in Tabriz, Iran. Earth Sci. Inform., 1\u201318.","DOI":"10.1007\/s12145-021-00617-2"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"146253","DOI":"10.1016\/j.scitotenv.2021.146253","article-title":"An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran","volume":"778","author":"Garajeh","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"312","DOI":"10.4236\/ars.2013.24034","article-title":"Development of a generic model for the detection of roof materials based on an object-based approach using WorldView-2 satellite imagery","volume":"2","author":"Taherzadeh","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_37","first-page":"79","article-title":"Optimal attributes for the object-based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery","volume":"32","author":"Fernandes","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","unstructured":"United Nations (2021, March 01). United Nations Launches Appeal for Iran Earthquake. Available online: http:\/\/www.un.org\/News\/Press\/docs\/2004\/iha852.doc.htm."},{"key":"ref_39","unstructured":"Solaymani Azad, S., Saboor, N., Moradi, M., Ajhdari, A., Youssefi, T., Mashal, M., and Roustaei, M. (2017). Preliminary Report on Geological Features of the Ezgaleh-Kermanshah Earthquake (M~7.3), November 12, 2017, West Iran, SSD of GSI Preliminary Report."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., Matsuoka, M., Miyajima, M., Adriano, B., Fallahi, A., and Karashi, J. (2018). Sequential SAR Coherence Method for the Monitoring of Buildings in Sarpole-Zahab, Iran. Remote Sens., 10.","DOI":"10.3390\/rs10081255"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.gsf.2019.10.008","article-title":"Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?","volume":"11","author":"Pourghasemi","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.scitotenv.2019.07.203","article-title":"Multi-hazard probability assessment and mapping in Iran","volume":"692","author":"Pourghasemi","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_43","unstructured":"Central Office of Natural Resources and Watershed Management in the Jahrom Township (2015). Hydrology and Flood, Technical Report."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-016-5665-9","article-title":"Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon\u2019s entropy, statistical index, and weighting factor models","volume":"188","author":"Khosravi","year":"2016","journal-title":"Environ. Monitor. Assess."},{"key":"ref_45","first-page":"287","article-title":"Temporary housing pattern based on grounded theory method (Case study: Sarpol-Zahab city after the earthquake 2017)","volume":"6","author":"Hoshyar","year":"2020","journal-title":"Environ. Hazard Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.5194\/isprs-archives-XLII-3-1857-2018","article-title":"SAR coherence change detection of urban areas affected by disasters using sentinel-1 imagery","volume":"XLII-3","author":"Washaya","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11069-020-03991-0","article-title":"Seismic damage assessment in Sarpole-Zahab town (Iran) using synthetic aperture radar (SAR) images and texture analysis","volume":"103","author":"Hajeb","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.tecto.2012.01.022","article-title":"Building the Zagros collisional orogen: Timing, strain distribution and the dynamics of Arabia\/Eurasia plate convergence","volume":"532","author":"Mouthereau","year":"2012","journal-title":"Tectonophysics"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hasanlou, M., Shah-Hosseini, R., Seydi, S., Karimzadeh, S., and Matsuoka, M. (2021). Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13061195"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"6853","DOI":"10.1029\/2018GL078577","article-title":"Geodetic constraints of the 2017 Mw7. 3 Sarpol Zahab, Iran earthquake, and its implications on the structure and mechanics of the northwest Zagros thrust-fold belt","volume":"45","author":"Feng","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., Matsuoka, M., Kuang, J., and Ge, L. (2019). Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective. ISPRS Int. J. Geoinf., 8.","DOI":"10.3390\/ijgi8100462"},{"key":"ref_52","first-page":"22","article-title":"Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest","volume":"61","author":"Tian","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","unstructured":"Geo-Informatics and Space Technology Development Agency (2019, September 09). Worldview-2. Available online: https:\/\/www.gistda.or.th\/main\/system\/files_force\/satellite\/104\/file\/2534-m-worldview2-datasheet.pdf?download=1.pdf."},{"key":"ref_54","unstructured":"WorldView-2 (2019, April 04). WorldView-2 Satellite Sensor. Available online: https:\/\/www.satimagingcorp.com\/satellite-sensors\/worldview-2\/."},{"key":"ref_55","unstructured":"Digital Globe (2011, July 20). The Benefits of the 8 Spectral Bands of WorldView-2. Available online: http:\/\/worldview2.digitalglobe.com\/docs\/WorldView-2_8-Band_Applications_Whitepaper.pdf."},{"key":"ref_56","unstructured":"Zhou, H., Liu, Q., Xu, Q., and Wang, Y. (October, January 26). Pan-Sharpening with a CNN-Based Two Stage Ratio Enhancement Method. Proceedings of the IGARSS IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"150","DOI":"10.30897\/ijegeo.834760","article-title":"Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images","volume":"8","author":"Wang","year":"2021","journal-title":"Int. J. Environ. Geoinform."},{"key":"ref_58","first-page":"26","article-title":"Assessment of Suitable Image Fusion Method for CARTOSAT-2E Satellite Urban Imagery for Automatic Feature Extraction","volume":"63","author":"Pendyala","year":"2020","journal-title":"Adv. Model. Anal. B"},{"key":"ref_59","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."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4421","DOI":"10.1080\/01431161.2020.1718237","article-title":"Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study","volume":"41","author":"Fallatah","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kuffer, M., Pfeffer, K., and Sliuzas, R. (2016). Slums from Space\u201415 Years of Slum Mapping Using Remote Sensing. Remote Sens., 8.","DOI":"10.3390\/rs8060455"},{"key":"ref_62","unstructured":"Bendini, N., Fonseca, M., Soares, R., Rufin, P., Schwieder, M., Rodrigues, A., and Hostert, P. (October, January 26). Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado. Proceedings of the IGARSS, IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lang, S. (2008). Object-based image analysis for remote sensing applications: Modeling reality\u2014Dealing with complexity. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_1"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6117","DOI":"10.1080\/01431161.2018.1454621","article-title":"Object-based satellite image analysis applied for crop residue estimating using Landsat OLI imagery","volume":"39","author":"Najafi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/LGRS.2017.2763979","article-title":"A Novel Approach of Fuzzy Dempster\u2013Shafer Theory for Spatial Uncertainty Analysis and Accuracy Assessment of Object-Based Image Classification","volume":"15","author":"Feizizadeh","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1080\/15481603.2017.1328758","article-title":"Developing soil indices based on brightness, darkness, and greenness to improve land surface mapping accuracy","volume":"54","author":"Qiu","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/JSTARS.2014.2350036","article-title":"Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran","volume":"7","author":"Blaschke","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","first-page":"187","article-title":"Assessing object-based classification: Advantages and limitations","volume":"4","author":"Desheng","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/JSTARS.2014.2298876","article-title":"Automatic Framework for Spectral\u2013Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles","volume":"7","author":"Ghamisi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4106","DOI":"10.1109\/TGRS.2016.2536687","article-title":"Automatic Object-Based Image Classification Using Complex Diffusions and a New Distance Metric","volume":"54","author":"Zehtabian","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/01431161.2012.703343","article-title":"Rule-based impervious surface mapping using high spatial resolution imagery","volume":"34","author":"Xu","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3380","DOI":"10.1080\/01431161.2015.1060645","article-title":"Detailed intra-urban mapping through transferable OBIA rule sets using WorldView-2 very-high-resolution satellite images","volume":"36","author":"Hamedianfar","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.ecolind.2016.01.006","article-title":"Developing indices of temporal dispersion and continuity to map natural vegetation","volume":"64","author":"Qiu","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.geomorph.2017.06.002","article-title":"Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes","volume":"293","author":"Feizizadeh","year":"2017","journal-title":"Geomorphology"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"448","DOI":"10.2307\/634969","article-title":"Remote Sensing and Image Interpretation","volume":"146","author":"Wright","year":"1980","journal-title":"Geogr. J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.soildyn.2014.06.026","article-title":"A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz","volume":"66","author":"Karimzadeh","year":"2014","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., and Mastuoka, M. (2017). Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy. Remote Sens., 9.","DOI":"10.3390\/rs9040330"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., and Matsuoka, M. (2018). A Weighted Overlay Method for Liquefaction-Related Urban DamageDetection: A Case Study of the 6 September 2018 Hokkaido Eastern Iburi Earthquake, Japan. Geosciences, 8.","DOI":"10.3390\/geosciences8120487"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1193\/033014EQS042M","article-title":"Building Damage Assessment Using High-Resolution Satellite SAR Images of the 2010 Haiti Earthquake","volume":"32","author":"Miura","year":"2016","journal-title":"Earthq. Spectra"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2013.06.011","article-title":"A comprehensive review of earthquake-induced building damage detection with remote sensing techniques","volume":"84","author":"Dong","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:22Z","timestamp":1760167342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,24]]},"references-count":84,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214272"],"URL":"https:\/\/doi.org\/10.3390\/rs13214272","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,24]]}}}