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Salient object detection in optical remote sensing images (RSI-SOD) aims to segment objects that attract human attention in optical RSIs. With the tremendous success of full convolutional neural networks (FCNs) for pixel-level segmentation, the performance of RSI-SOD has improved significantly. However, most RSI-SOD methods primarily focus on enhancing detection accuracy, neglecting memory and computational costs, which hinders their deployment in resource-constrained applications. In this paper, we propose a novel lightweight RSI-SOD network, named MEANet, to address these challenges. Specifically, a multiscale edge-embedded attention (MEA) module is designed to enhance the capture of salient objects by incorporating edge information into spatial attention maps. Building upon this module, a U-shaped decoder network is constructed, and a multilevel semantic guidance (MSG) module is introduced to mitigate the issue of semantic dilution in U-shaped networks. Through extensive quantitative and qualitative comparisons with 27 state-of-the-art FCN-based models, the proposed model demonstrates competitive or superior performance, while maintaining only 3.27M parameters and 9.62G FLOPs. The code and results of our method are available at https://github.com/LiangBoCheng/MEANet.
Liang et al. (Tue,) studied this question.