Lightweight Edge-Aware Mamba-Fusion Network for Weakly Supervised Salient Object Detection in Optical Remote Sensing Images

Gaojie Xing, Mengyin Wang, Fasheng Wang, Fuming Sun, Haojie Li

Abstract: Despite the significant progress made in fully supervised salient object detection in optical remote sensing images (ORSI-SOD), these methods rely heavily on pixel-level annotations, which are time-consuming and labor-intensive. This situation has driven the development of weakly supervised ORSI-SOD methods. However, existing weakly supervised ORSI-SOD methods still face excessive model parameters and high computational complexity, hindering their flexibility and deployment in edge devices. To address these challenges, we propose the LightEMNet, a scribble-based, lightweight, and high-performance edge-aware network for ORSI-SOD. The network employs MobileNetV2 as its lightweight encoder backbone. To mitigate the suboptimal feature extraction performance caused by the lightweight architecture, we design a feature refinement layer (FRL) to refine the features extracted from the backbone, thereby generating guidance information while achieving better structural awareness and object localization. To realize better detail optimization, we introduce edge information extracted by a multiscale edge perception module (MEP) to regulate high-level features. Finally, considering the shortcomings of traditional convolution in global-awareness, we propose a Mamba-based cross-scale edge-semantic interaction (CESI) module to achieve efficient alignment of semantics and edges, which consequently enhances the representation consistency of the fused features and improves the model’s adaptability to complex scenes. We verify the effectiveness of the LightEMNet through extensive experiments. The results demonstrate that the proposed LightEMNet exhibits competitive detection performance with only 4.81 M parameters. Codes and results are available at https://github.com/xingggao/LightEMNet

External IDs:dblp:journals/tgrs/XingWWSL25