Automatic identification of typical landslides in Baxie River Basin based on region growing
ZHANG Shuai1,2, ZHAO Shulan3, LEI Xiaozhang1,2, FU Wenxi1,2, CHEN Zhuo1,2
1. College of Water Resource & Hydropower, Sichuan University, 610065, Chengdu, China; 2. State Key Lab of Hydraulics and Mountain River Engineering, Sichuan University, 610065, Chengdu, China; 3. Chengdu Shude High School, 610035, Chengdu, China
Abstract：[Background] The Loess Plateau is prone to a large number of landslides under the condition of concentrated rainfall in summer, which causes huge damage to local people's lives and property, rapid extraction of landslide information plays an important role in disaster response. At present, there are a large number of landslides in the Baxie River Basin of the Loess Plateau, and most of them are typical landslide. Traditional manual interpretation can effectively identify landslides, but its efficiency is extremely low. In addition, due to the restrictions of terrain and weather, the manual interpretation of typical landslides is more difficult.[Methods] On study area Baxie River Basin in the Loess Plateau, using Google Earth software to extract the images of study area,and Gaussian filter was adopted to reduce the influence of image noise on recognition results. The gray value range of the trailing edge of the interpreted landslide in the study area was used as the condition for automatic selection of seed points, then using region growing algorithm and morphological operations to realize recognition and trailing edge boundary extraction of landslides in typical landslides that has not been interpreted in the study area. Optimal region growth conditions were obtained by adjusting the growth threshold.[Results] This method automatically identified the typical landslide in the study area.The combination of morphologic open operation and close operation removed the obvious sawtooth and small burrs in the results and link the external broken lines to smooth the edges. Sobel operator combined with watershed algorithm extracted effectively the landslide's trailing edge boundary. Correct identification, wrong identification and missing identification existed in the extraction process of landslide trailing edge boundary. With the increase of the growth threshold, the divergence factor increased and the error factor decreased, indicating that the over-recognition increased and the missing recognition decreased. Three images of the Baxie River Basin in the Loess Plateau were extracted by Google Earth. The three images were identified and the landslide trailing edge was extracted by this method. Meanwhile, the three images were interpreted manually and the landslide trailing edge was drawn to verify the identification results. When the gray growth threshold is 10, the accuracy of identifying the trailing edge of landslide reached 78.94%. For new landslides, the grayscale characteristics at the back edge of the landslide body were obvious, and the identification results were good. For landslides with a long occurrence time, the surface weathering and new vegetation growth will reduce the identification accuracy of this method.[Conculsions] These suggest that the proposed method is effective to identify the typical landslides in Baxie River Basin of Loess Plateau, which provides a direction for landslide disaster assessment, rescue and reconstruction.
张帅, 赵书兰, 雷孝章, 符文熹, 陈卓. 基于区域生长算法巴谢河流域典型滑坡自动识别[J]. 中国水土保持科学, 2021, 19(3): 103-109.
ZHANG Shuai, ZHAO Shulan, LEI Xiaozhang, FU Wenxi, CHEN Zhuo. Automatic identification of typical landslides in Baxie River Basin based on region growing. SSWC, 2021, 19(3): 103-109.
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2021, Vol. 19 
