{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:21:06Z","timestamp":1772907666475,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971356, 61801395, 61971273, and 62071384"],"award-info":[{"award-number":["61971356, 61801395, 61971273, and 62071384"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2020GM-137"],"award-info":[{"award-number":["2020GM-137"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia\u2013Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand.<\/jats:p>","DOI":"10.3390\/rs13040633","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:12:10Z","timestamp":1613146330000},"page":"633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7230-1476","authenticated-orcid":false,"given":"Xiuwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7063-6747","authenticated-orcid":false,"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710072, China"}]},{"given":"Jiaojiao","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710072, China"}]},{"given":"Yafei","family":"Wang","sequence":"additional","affiliation":[{"name":"Ningxia\u2013Inner Mongolia Hydrology and Water Resource Bureau, Baotou 014030, China"}]},{"given":"Minhao","family":"Fan","sequence":"additional","affiliation":[{"name":"Hydrology Bureau of the Yellow River Conservancy Commission, Zhengzhou 450004, China"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Xi\u2019an Jiaotong University, Xi\u2019an 710072, China"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11069-016-2580-x","article-title":"Dangerous ice phenomena on the lowland rivers of European Russia","volume":"88","author":"Agafonova","year":"2017","journal-title":"Nat. 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