  Salient object detection (SOD) in optical remote sensing images (RSIs), or
RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to
the difference between optical RSIs and natural scene images (NSIs), directly
applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results.
In this paper, we propose a novel Adjacent Context Coordination Network
(ACCoNet) to explore the coordination of adjacent features in an
encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of
three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and a
decoder. As the key component of ACCoNet, ACCoM activates the salient regions
of output features of the encoder and transmits them to the decoder. ACCoM
contains a local branch and two adjacent branches to coordinate the multi-level
features simultaneously. The local branch highlights the salient regions in an
adaptive way, while the adjacent branches introduce global information of
adjacent levels to enhance salient regions. Additionally, to extend the
capabilities of the classic decoder block (i.e., several cascaded convolutional
layers), we extend it with two bifurcations and propose a
Bifurcation-Aggregation Block to capture the contextual information in the
decoder. Extensive experiments on two benchmark datasets demonstrate that the
proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation
metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and
results of our method are available at https://github.com/MathLee/ACCoNet.
