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Open AccessArticle

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

by Xiuwei Zhang 1,2,†, Jiaojiao Jin 1,2,†, Zeze Lan 1,2,*, Chunjiang Li 3, Minhao Fan 4, Yafei Wang 5, Xin Yu 3 and Yanning Zhang 1,2
1
School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi’an 710072, China
3
Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China
4
Hydrology Bureau of the Yellow River Conservancy Commission, Zhengzhou 450004, China
5
Ningxia–Inner Mongolia Hydrology and Water Resource Bureau, Baotou 014030, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(2), 221; https://doi.org/10.3390/rs12020221
Received: 30 October 2019 / Revised: 16 December 2019 / Accepted: 3 January 2020 / Published: 9 January 2020
River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset. View Full-Text
Keywords: river ice; position attention; channel-wise attention; deep convolutional neural network; semantic segmentation river ice; position attention; channel-wise attention; deep convolutional neural network; semantic segmentation
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Zhang, X.; Jin, J.; Lan, Z.; Li, C.; Fan, M.; Wang, Y.; Yu, X.; Zhang, Y. ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features. Remote Sens. 2020, 12, 221.

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