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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,6,21]]},"abstract":"<jats:p>Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning\u2013based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention\u2013based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.<\/jats:p>","DOI":"10.1145\/3454009","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T18:07:12Z","timestamp":1624298832000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["MILL: Channel Attention\u2013based Deep Multiple Instance Learning for Landslide Recognition"],"prefix":"10.1145","volume":"17","author":[{"given":"Xiaochuan","family":"Tang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China and University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhe","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzhen","family":"Ju","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weile","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2011.05.010"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10346-015-0557-6"},{"key":"e_1_2_1_3_1","volume-title":"An ensemble approach to multi-view multi-instance learning. 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