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

• URL: http://arxiv.org/abs/2205.08959v1
• Date: Wed, 18 May 2022 14:32:47 GMT
• Title: A lightweight multi-scale context network for salient object detection in optical remote sensing images
• Authors: Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, Huiyu Zhou
• Abstract summary: We propose a multi-scale context network, namely MSCNet, for salient object detection in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects. In order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism.
• Score: 16.933770557853077
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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