A Transfer Learning Remote Sensing Landslide Image Segmentation Method Based on Nonlinear Modeling and Large Kernel Attention

Abstract

Image segmentation plays a key role in remote sensing, particularly in landslide image segmentation. Remote sensing of landslide images is challenging due to their single category but complex detailed features, making it difficult to determine landslide boundaries and extents. Traditional segmentation methods often yield poor results for such images. To address these challenges, we propose the Large Kernel Nested UKAN (LKN-UKAN). The key contributions and findings are as follows. (1) We embed a Tokenized KAN Block (Tok-KAN) in U-Net++ to enhance complex feature modeling, leveraging Tok-KAN’s strengths in nonlinear modeling and relationship capture. (2) We design a Dual Large Fusion Selective Kernel Attention (DLFFSKA) module to improve global perception and contextual information capture. (3) We apply transfer learning to transfer feature-rich remote sensing image features to landslide data, significantly improving segmentation performance. The experimental results demonstrate that the LKN-UKAN achieved significant improvements in remote sensing landslide image segmentation compared with state-of-the-art methods, particularly in terms of boundary accuracy and feature representation.