The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 645–650, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-645-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 645–650, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-645-2020

  12 Aug 2020

12 Aug 2020

SPRING POINT DETECTION OF HIGH RESOLUTION IMAGE BASED ON YOLOV3

J. Wu1, Z. Zhang2, G. Huang3, and G. Ma4 J. Wu et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China
  • 2Comprehensive Survey Center for Natural Resources, China Geological Survey, Xining, Qinghai, China
  • 3The Second Military Representative Office of the Rocket Army Equipment Department in Wuhan, China
  • 4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China

Keywords: Spring point, YOLOv3, Target detection, Xinjiang region, Network framework, The Belt and Road Initiatives

Abstract. The Xinjiang region of China is a vast and sparsely populated area with complex topography, surrounded by basins and mountains, and its geomorphological features and water circulation process make the traditional spring water resource acquisition time-consuming, labor-consuming and inaccurate. Remote Sensing Technology has the advantages of large scale, periodicity, timeliness and comprehensiveness in target detection. In order to realize the artificial intelligence detection of springs in Xinjiang, this paper presents a method of detecting springs in remote sensing image based on the YOLOV3 network framework, based on the data set of 512 * 512 by using 0.8m remote sensing image annotation, a model of recognition of spring point based on Yolov3 network is constructed and trained. The results show that the map of spring point is 0.973, which is the basis of monitoring and protecting the natural environment in the Belt and Road Initiatives.