A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion

Abstract

Landslides pose significant risks as natural disasters, highlighting the importance of accurate mapping using remote sensing images for various practical applications. However, due to the challenges arising from incomplete and inaccurate boundary information of foreground landslide polygons, existing methods can only achieve suboptimal performance. To this premise, in this paper, we propose a segmentation network called GMNet that leverages global information extraction and multi-scale feature fusion to enhance the discrimination of landslides from other objects. Specifically, by employing a multi-branch mechanism, our method effectively captures global information, while an improved multi-scale feature fusion technique addresses the issue of varying scales in landslide polygons. Furthermore, semantic enhancement enhances the semantic information of low-level features, bridging the semantic gap and enhancing fusion efficacy. Experimental results demonstrate the effectiveness of our network in segmenting landslide areas accurately within the remote sensing image dataset. Especially, our F1_scores on three benchmarks outperform existing runner-ups by notable margins of 4.81%, 1.72%, and 1.16%, showcasing the value of our method in this domain.