AUTOMATIC BENGGANG RECOGNITION BASED ON LATENT SEMANTIC FUSION OF UHR DOM AND DSM FEATURES
- 1Deptartment of Soil and Water Conservation, Changjiang River Scientific Research Institute (CRSRI), Wuhan, China
- 2Research Center on Mountain Torrent & Geologic Disaster Prevention of the Ministry of Water Resources, Wuhan, China
- 3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Keywords: Benggang, DOM, DSM, BoV-TW, LDA, Automatic Recognition
Abstract. Benggang are characterized by deep-cut slopes with various shapes and depressions on the vast weathered crust slopes in southern China. The gully heads have been continuously collapsed and eroded to form a chair-like erosion landforms. It develops rapidly, and leads to large amounts of erosion, with the hazards of damaging land resources, destroying basic farmland, and deteriorating ecological environment. To study and manage Benggang, the primary task is to discover it. Traditional methods based on local in-situ investigations, which are not only labour-consuming but also inefficient. These methods are difficult to meet the needs of large-scale investigations of Benggang. This paper proposes a method for automatic Benggang recognition based on Ultra-High Resolution (UHR) DOM (Digital Orthophoto Map) and DSM (Digital Surface Model) obtained from UAV (Unmanned Aerial Vehicle) survey. This method adopts a Bag of Visual-Topographical Words (BoV-TW) model. The local features extracted from DOM and DSM are represented based on BoV-TW, and fused by Latent Dirichlet Allocation (LDA). Finally Support Vector Machine (SVM) is adopted as a supervised classifier to achieve high-precision automatic Benggang recognition. Experimental results prove that the total accuracy of our method can be maintained at about 95%, with recall and precision above 80% (the highest are 97.22% and 94.44%, respectively), which are significantly higher than the methods of using only DOM local features and using only BoV-TW.