文献类型：会议论文

英文题名：Sentinel 1a-2a Incorporating an Object-Based Image Analysis Method for Flood Mapping and Extent Assessment

作者：Azhand, Donya[1,2]; Pirasteh, Saied[1]; Varshosaz, Masood[1,3]; Shahabi, Hejar[2,4]; Abdollahabadi, Salimeh[5]; Teimouri, Hossein[6]; Pirnazar, Mojtaba[2]; Wang, Xiuqing[1]; Li, Weilian[7]

机构：[1] Shaoxing Institute of Artificial Intelligence, Shaoxing University, China, China; [2] Mapnagroup [Monenco Iran Consulting Engineers], No 12, Attar St., Vanek Square, Valiasr St., Tehran, Iran; [3] Department of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran, 15433-19967, Iran; [4] Institute of Geography, Slovak Academy of Sciences, ?tefániková 49, Bratislava, 814 73, Slovakia; [5] Department of Gis, Neyshabour Water and Wastewater Company, Neyshabour, Iran; [6] Department of Geography, University of Tehran, Tehran, Iran; [7] Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, China

会议日期：May 13, 2024 - May 17, 2024

会议地点：Changsha, China

语种：英文

外文关键词：Change detection - Farms - Floods - Global positioning system - Image segmentation - Mapping - Object detection

外文摘要：This study presents flood extent extraction and mapping from Sentinel images. Here we suggest an algorithm for extracting flooded areas from object-based image analysis (OBIA) using Sentinel-1A and Sentinel-2A images to map and assess the flood extent from the beginning to one week after the event. This study used multi-scale parameters in OBIA for image segmentation. First, we identified the flooded regions by applying our proposed algorithm on the Sentinel-1A. Then, to evaluate the effects of the flood on each land-use/land cover (LULC) class, Sentinel-2A images is classified using the OBIA after the event. Besides, we also used the threshold method to compare the proposed algorithm applying OBIA to determine the efficiency in computing parameters for change detection and flood extent mapping. The findings revealed the best performance for the segmentation process with an Object Fitness Index (OFI) is 0.92 when the scale parameter of 60 is applied. The results also show that 2099.4 km2 of the study area is flooded at the beginning of the flood. Furthermore, we found that the most flooded LULC classes are agricultural land and orchards with 695.28km2 (32.4%) and 708.63 km2 (33.7%), respectively. In comparison, about 33.9% of the remaining flooded area has occurred in other classes (i.e., fish farm, built-up, bare land and water bodies). The resulting object of each scale parameter was evaluated by Object Pureness Index (OPI), Object Matching Index (OMI), and OFI. Finally, our Overall Accuracy (OA) method incorporated field data using the Global Positioning System (GPS) shows 93%, 90%, and 89% for LULC, flood map (i.e., using our proposed algorithm), and threshold method, respectively. ? 2024 Donya Azhand et al.