SEGNET-BASED EXTRACTION OF WETLAND VEGETATION INFORMATION FROM UAV IMAGES
- 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.