ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 381–386, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-381-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 381–386, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-381-2020

  03 Aug 2020

03 Aug 2020

GLACIER IDENTIFICATION FROM LANDSAT8 OLI IMAGERY USING DEEP U-NET

Q. He1, Z. Zhang2, G. Ma1, and J. Wu1 Q. He et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079, Wuhan, Hubei, China
  • 2Xining Comprehensive Survey Center for Natural Resources, China Geological Survey, 810000, Xining, Qinghai, China

Keywords: Glacier identification, Landsat8 OLI, U-Net, NDSI, Semantic segmentation, Remote sensing

Abstract. Glacier is one of the clearest signal of climate change, and its changes have important effects on regional climate and water resources. Glacier identification is the basic of glacial changes research. Traditional remote sensing glacier identification methods usually perform simple bands calculation based on the spectral characteristics of glacier. The identification results are greatly affected by threshold segmentation. In addition, there is a misclassification of water body and glacier. As a simple and efficient semantic segmentation network, U-Net has been widely used in many fields of image processing. This paper performs an improved semantic segmentation network Deep U-Net for glacier identification using Landsat 8 OLI image as the data source, and compares it with the traditional NDSI glacier identification method. The identification results are validated by the glacier label data produced by visual interpretation. The results indicate that the proposed method achieves an identification accuracy of 97.27%, which is higher than the NDSI glacier identification method. It can effectively exclude the interference of water bodies on glacier identification, and has a higher degree of automation.