Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: An Application to Brazil

Author(s)

Xu, Guosen ; Wang, Yi ; Wang, Lizhe ; Soares, Lucas Pedrosa ; Grohmann, Carlos H.

• Document type: Journal article
• Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING; v. 15, p. 16-pg., 2022-01-01.

Abstract

Landslides have caused tremendous damage to human lives and property safety. However, the complex environment of mountain landslides and the vegetation coverage around landslides make it difficult to identify landslides quickly and efficiently using high-resolution images. To address this challenge, this article presents a feature-based constraint deep U-Net (FCDU-Net) method to detect rainfall-induced mountainous landslides. Usually, the vegetation in the landslide area is severely damaged, and the vegetation coverage can indirectly reflect the spatial extent of the landslide. Meanwhile, the texture features of high-resolution images can characterize the surface environment of landslide hazards to a certain extent. We first introduce auxiliary features of normalized difference vegetation index and gray-level co-occurrence matrix into the proposed method to further improve the detection performance. Then, to minimize the information redundancy of these features and the image, we combine Relief-F and Deep U-Net to screen the optimal features to effectively identify accurate and detailed landslide boundaries. Compared with traditional semantic segmentation methods, the FCDU-Net method can capture fine-grained details in high-resolution images and produce more accurate segmentation results. We conducted experiments by applying the proposed method and other most popular semantic segmentation methods to a high-resolution RapidEye image in Rio de Janeiro, Brazil. The results demonstrate that the FCDU-Net method can achieve better landslide detection results than the other semantic segmentation methods, and the evaluation measures of Precision, F1 score, and mean Intersection-over-Union are as high as 88.87%, 81.17%, and 83.19%, respectively. Furthermore, we quantitatively analyze the effect of the convolution input window size on the performance of FCDU-Net in detecting landslides. We believe that FCDU-Net can serve as a reliable tool for fast and accurate regional landslide hazard surveys. (AU)

• FAPESP's process: 19/17555-1 - Automatic segmentation of landslide scars in remote sensing imagery with deep learning
• Grantee: Lucas Pedrosa Soares
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 16/06628-0 - Application of high-resolution digital elevation models in geology and geomorphology
• Grantee: Carlos Henrique Grohmann de Carvalho
• Support Opportunities: Regular Research Grants