Automatic extraction of small mountain river information and width based on China-made GF-1 satellites remote sensing images

doi:10.13474/j.cnki.11-2246.2020.0069

Abstract

Abstract: Extraction of high-resolution geomorphic information from remote sensing images is a key technology for supporting the research of mountain rivers. In this research, we propose a DEM-aided approach based on object-based image analysis and improved decision tree classification for water information extraction and present a method for automatic extraction of small river width. We used the 1421 km² area of upstream of Huangfuchuan River Basin on the Loess Plateau, China, as a case study area. The China-made GF-1 satellite images and the DEM data are implemented as the secondary data source. The results show that the proposed method has a total accuracy as 89.5%. For extremely small rivers with width ranging from 0 to 10 meters, the error of river width extraction by our method is 18.54%. The extraction error of small rivers whose width ranging from 10 to 30 meters is 12.07%.

Key words: GF-1 satellite images, small mountain rivers, river width, automatic extraction method, improved decision tree classification

CLC Number:

XUE Yuan, LI Dan, WU Baosheng, FU Xudong. Automatic extraction of small mountain river information and width based on China-made GF-1 satellites remote sensing images[J]. Bulletin of Surveying and Mapping, 2020, 0(3): 12-16.

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