Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162650
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dc.contributor.authorLi, Gongyangen_US
dc.contributor.authorLiu, Zhien_US
dc.contributor.authorLin, Weisien_US
dc.contributor.authorLing, Haibinen_US
dc.date.accessioned2022-11-02T02:16:07Z-
dc.date.available2022-11-02T02:16:07Z-
dc.date.issued2021-
dc.identifier.citationLi, G., Liu, Z., Lin, W. & Ling, H. (2021). Multi-content complementation network for salient object detection in optical remote sensing images. IEEE Transactions On Geoscience and Remote Sensing, 60. https://dx.doi.org/10.1109/TGRS.2021.3131221en_US
dc.identifier.issn0196-2892en_US
dc.identifier.urihttps://hdl.handle.net/10356/162650-
dc.description.abstractIn the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. The unique characteristics of optical RSIs, such as scales, illuminations and imaging orientations, bring significant differences between NSI-SOD and RSI-SOD. In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. Specifically, MCCNet is based on the general encoder-decoder architecture, and contains a novel key component named Multi-Content Complementation Module (MCCM), which bridges the encoder and the decoder. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features, and exploit the content complementarity between them to highlight salient regions over various scales in RSI features through the attention mechanism. Besides, we comprehensively introduce pixel-level, map-level and metric-aware losses in the training phase. Extensive experiments on two popular datasets demonstrate that the proposed MCCNet outperforms 23 state-of-the-art methods, including both NSI-SOD and RSI-SOD methods. The code and results of our method are available at https://github.com/MathLee/MCCNet.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE2016-T2-2-057(S)en_US
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensingen_US
dc.rights© 2021 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleMulti-content complementation network for salient object detection in optical remote sensing imagesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TGRS.2021.3131221-
dc.identifier.scopus2-s2.0-85120552603-
dc.identifier.volume60en_US
dc.subject.keywordsMulti-Content Complementationen_US
dc.subject.keywordsOptical Remote Sensing Imagesen_US
dc.description.acknowledgementThis work was supported in part by the National Natural Science Foundation of China under Grant 62171269, in part by the China Scholarship Council under Grant 202006890079, and in part by the Singapore Ministry of Education Tier-2 Fund MOE2016-T2-2-057(S).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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