  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.
