{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:57:27Z","timestamp":1760237847521,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2018YFC0407804"],"award-info":[{"award-number":["2018YFC0407804"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment.<\/jats:p>","DOI":"10.3390\/rs12132068","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:17:17Z","timestamp":1593429437000},"page":"2068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Spatial Attraction Models Coupled with Elman Neural Networks for Enhancing Sub-Pixel Urban Inundation Mapping"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2185-8407","authenticated-orcid":false,"given":"Linyi","family":"Li","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Yun","family":"Chen","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land and Water, Canberra 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4860-1679","authenticated-orcid":false,"given":"Tingbao","family":"Xu","sequence":"additional","affiliation":[{"name":"Fenner School of Environment and Society, The Australian National University, Canberra 2601, Australia"}]},{"given":"Lingkui","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-4581","authenticated-orcid":false,"given":"Chang","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Kaifang","family":"Shi","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.isprsjprs.2019.04.014","article-title":"Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence","volume":"152","author":"Li","year":"2019","journal-title":"ISPRS J. 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