MF-L-UNet++: A Multi-Scale Feature Recognition Algorithm for Landslide Images

• Published in: IEICE Transactions on Information and Systems Vol. E108.D; no. 9; pp. 1058 - 1071
• Main Authors: LI, Jiajun, LI, Qiang, ZHENG, Kui, LU, Jinzheng, WEI, Lijuan, XIANG, Qiang
• Format: Journal Article
• Language: English
• Published: The Institute of Electronics, Information and Communication Engineers 01.09.2025; 一般社団法人 電子情報通信学会
• Subjects: feature fusion; image segmentation; landslide; multi-scale feature
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.2024EDP7280

For landslides, a serious natural disaster, how to accurately locate the landslide area is crucial for disaster mitigation and relief work. In view of the complex situation of landslides and the difficulty of traditional methods in quickly and accurately determining the area where landslides occur, this paper proposes a multi-scale feature recognition algorithm for landslide images (MF-L-UNet++) by analyzing the characteristics of landslides and common semantic segmentation networks. MF-L-UNet++ is based on UNet++ with the following modifications. First, the Dual Large Feature Fusion Selective Kernel Attention (DLFFSKA) module is employed to eliminate the interference of background in model recognition and enhance the accuracy of landslide location capture. Second, the Same Scale Lightweight Kernel Prediction (SSLKP) is designed to achieve a significant reduction in the number of parameters while reducing the loss of convolutional feature information and position offset. Third, Large Kernel Content Aware Recombination Upsample (LKCARU) is presented to enhance the model’s capacity to delineate the boundaries and details of the landslide, thereby facilitating more precise segmentation outcomes. Finally, Atrous Spatial Pyramid Pooling (ASPP) is introduced to address the issue of inadequate coverage and fusion of multi-scale information following the utilization of multiple modules, enabling the model to fully integrate global context information. The experimental results showed that on the expanded Bijie Landslide Dataset, the algorithm proposed in this study achieved an improvement of 3.68%, 1.29%, and 1.59% in IoU, Precision, and F1-score, respectively, compared to the UNet++ algorithm, while Params and Loss decreased by 0.86M and 0.05, respectively. Compared to other commonly used segmentation methods, the detection performance of the model in this paper is at the optimal level.