A lightweight multi-scale context network for salient object detection in optical remote sensing images

TL;DR

This paper introduces MSCNet, a lightweight multi-scale context network designed for salient object detection in optical remote sensing images, effectively handling scale variations and complex backgrounds.

Contribution

The paper proposes a novel multi-scale context extraction and attention-based feature aggregation mechanism specifically tailored for optical remote sensing image SOD.

Findings

Achieves competitive performance with only 3.26 million parameters.

Effectively handles scale variations in salient objects.

Demonstrates superior detection accuracy on benchmark datasets.

Abstract

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.