International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 375–380, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-375-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 375–380, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-375-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

SEGNET-BASED EXTRACTION OF WETLAND VEGETATION INFORMATION FROM UAV IMAGES

T. Y. Tang, B. L. Fu, P. Q. Lou, and L. Bi T. Y. Tang et al.
  • Guilin University of Technology, 541006, Guangxi, China

Keywords: Multi-class SegNet model, Fusion SegNet model, wetland vegetation, extraction multi-classification, UAV

Abstract. This study takes Guangxi Huixian National Wetland Park as the research area, and uses the UAV image and ground measured tag data as the data source. The SegNet model is used to extract the wetland vegetation information in the study area, further verification multiple classification SegNet model and fusion multiple SegNet model of single/double classification precision of the two ways of extracting karst wetland vegetation information. The experimental results show that the Kappa coefficient of the multi-segmented SegNet model is 0.68, while the multi-class SegNet model has a classification effect of 0.59. The classification effect of the karst wetland vegetation information extracted by multiple single/double-class SegNet models is more than the multi-classification. The SegNet model has high precision.