Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162650
Title: Multi-content complementation network for salient object detection in optical remote sensing images
Authors: Li, Gongyang
Liu, Zhi
Lin, Weisi
Ling, Haibin
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Li, 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.2021.3131221
Project: MOE2016-T2-2-057(S)
Journal: IEEE Transactions on Geoscience and Remote Sensing
Abstract: In 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.
URI: https://hdl.handle.net/10356/162650
ISSN: 0196-2892
DOI: 10.1109/TGRS.2021.3131221
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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