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

Autor:	Lin, Yuhan, Sun, Han, Liu, Ningzhong, Bian, Yetong, Cen, Jun, Zhou, Huiyu
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted by ICPR2022, source code, see https://github.com/NuaaYH/MSCNet
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.08959 Zobrazit plný text záznamu View this record from Arxiv