{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T16:24:06Z","timestamp":1773678246467,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,3,19]],"date-time":"2015-03-19T00:00:00Z","timestamp":1426723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Ministry of Science, ICT, and Future Planning","award":["2011-0030879"],"award-info":[{"award-number":["2011-0030879"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper exploits an effective water extraction method using SAR imagery in preparation for flood mapping in unpredictable flood situations. The proposed method is based on the thresholding method using SAR amplitude, terrain information, and object-based classification techniques for noise removal. Since the water areas in SAR images have the lowest amplitude value, the thresholding method using SAR amplitude could effectively extract water bodies. However, the reflective properties of water areas in SAR imagery cannot distinguish the occluded areas caused by steep relief and they can be eliminated with terrain information. In spite of the thresholding method using SAR amplitude and terrain information, noises which interfered with users\u2019 interpretation of water maps still remained and the object-based classification using an object size criterion was applied for the noise removal and the criterion was determined by a histogram-based technique. When only using SAR amplitude information, the overall accuracy was 83.67%. However, using SAR amplitude, terrain information and the noise removal technique, the overall classification accuracy over the study area turned out to be 96.42%. In particular, user accuracy was improved by 46.00%.<\/jats:p>","DOI":"10.3390\/s150306652","type":"journal-article","created":{"date-parts":[[2015,3,19]],"date-time":"2015-03-19T10:38:57Z","timestamp":1426761537000},"page":"6652-6667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Water Area Extraction Using RADARSAT SAR Imagery Combined with Landsat Imagery and Terrain Information"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1891-8464","authenticated-orcid":false,"given":"Seunghwan","family":"Hong","sequence":"first","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, Seodaemun-gu,  Seoul 120-749, Korea"}]},{"given":"Hyoseon","family":"Jang","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, Seodaemun-gu,  Seoul 120-749, Korea"}]},{"given":"Namhoon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, Seodaemun-gu,  Seoul 120-749, Korea"}]},{"given":"Hong-Gyoo","family":"Sohn","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Yonsei University, Seodaemun-gu,  Seoul 120-749, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2015,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1080\/01431160010014729","article-title":"Integration of remote sensing data and GIS for accurate mapping of flooded areas","volume":"23","author":"Brivio","year":"2002","journal-title":"Int. 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