A lightweight multi-scale context network for salient object detection in optical remote sensing images
This work addresses a scarce but important problem in remote sensing image analysis, offering a lightweight solution for detecting salient objects in complex optical images.
The paper tackles salient object detection in optical remote sensing images, which is challenging due to multi-scale variations and complex backgrounds, by proposing MSCNet, a lightweight multi-scale context network that achieves competitive performance with only 3.26M parameters.
Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. The code will be available at https://github.com/NuaaYH/MSCNet.