A State Space Model-Driven Multiscale Attention Method for Geological Hazard Segmentation

Abstract

High-precision segmentation of geological disasters plays a crucial role in disaster rescue, significantly contributing to improving rescue efficiency and optimizing the allocation of rescue resources. However, landslides and debris flows typically have irregular contours and arbitrary scopes, which cause most existing methods to suffer from poor performance. To address these issues, we propose a novel dual-path feature extraction architecture for geological hazard segmentation. First, the state space model-driven global multiscale attention module (SSMGMA) is used to model cross-scale long-range dependencies by powerful multiscale representation. Dilated convolutions are adopted to extract multiscale features, while the state space model (SSM) is incorporated to capture the global context and model cross-scale long-range dependencies. Consequently, the SSMGMA allows the proposed model to completely segment geological disasters. Subsequently, the high-frequency prompt encoder module (HFPE) is employed to alleviate the negative effects caused by irregular contour problems. The core idea of the HFPE is to effectively encode high-frequency information as the prompt to the decoder. Specifically, a well-designed encoding strategy is adopted in the HFPE, which can transform subtle variations in high-frequency information into precise locations of disaster areas. By combining the SSMGMA and HFPE, the proposed dual-path architecture leverages the advantages of multiscale features and high-frequency feature encoding, significantly improving the accuracy of disaster segmentation. Experimental results show that the proposed method has superior performance.