SEGNET-BASED EXTRACTION OF WETLAND VEGETATION INFORMATION FROM UAV IMAGES

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...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-3/W10; pp. 375 - 380
Main Authors Tang, T. Y., Fu, B. L., Lou, P. Q., Bi, L.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 07.02.2020
Copernicus Publications
Subjects
Classification
Karst
Unmanned aerial vehicles
Vegetation
Wetlands
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Summary: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.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-3-W10-375-2020