Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data

作者：Bolin Fu, Man Liu, Hongchang He, Feiwu Lan, Xu He, Lilong Liu, Liangke Huang, Donglin Fan, Min Zhao, Zhenglei Jia · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2021 · DOI：10.1016/j.jag.2021.102553 · 被引用次数：46 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

Karst wetlands have the characteristics of small scale and poor stability. At present, the wetland is being severely damaged and its area is seriously degraded, and the accurate identification of vegetation communities is very important for the rapid assessment and management of karst wetland. In this paper, Huixian Karst National Wetland Park, located in Guilin city, China, was taken as the study area, the digital orthophoto map (DOM) and digital surface model (DSM) of UAV images were selected as the data sources, and the vegetation communities of karst wetland were classified by using the object-based Random Forest (RF)-Decision Tree (DT) algorithm and SegNet algorithm. When the object-based RF algorithm and SegNet algorithm were used for coarse classification of karst vegetation, the parameters (mtry, ntree) of the object-based RF algorithm were optimized, and the data dimensionality reduction and RFE variable selection algorithm were used for selecting feature, and the single-class SegNet model was integrated based on the soft voting method to improve the applicability of vegetation classification in karst wetlands. In the classification of vegetation communities in karst wetlands, the optimized object-based RF-DT algorithm were used to extract the vegetation communities in the Areas A, B, and C. The statistical analysis of the importance of the feature variables (spectral features, texture features, geometric features, and position features) of various types of land cove...