Comparison of the Applicability of J-M Distance Feature Selection Methods for Coastal Wetland Classification

作者：Xianmei Zhang, Xiaofeng Lin, Dongjie Fu, Yang Wang, Shaobo Sun, Fei Wang, Cuiping Wang, Zhongyong Xiao, Yiqiang Shi · 发表于：Water · 年份：2023 · DOI：10.3390/w15122212 · 被引用次数：17 · 研究领域：Land Use and Ecosystem Services、Coastal wetland ecosystem dynamics、Remote Sensing in Agriculture

Accurate determination of the spatial distribution of coastal wetlands is crucial for the management and conservation of ecosystems. Feature selection methods based on the Jeffries-Matusita (J-M) method include J-M distance with simple average ranking (JMave), J-M distance based on weights and correlations (JMimproved), and heuristic J-M distance (JMmc). However, as the impacts of these methods on wetland classification are different, their applicability has rarely been investigated. Based on the Google Earth Engine (GEE) and random forest (RF) classifier, this is a comparative analysis of the applicability of the JMave, JMimproved, and JMmc methods. The results show that the three methods compress feature dimensions and retain all feature types as much as possible. JMmc exhibits the most significant compression from a value of 35 to 15 (57.14%), which is 37.14% and 40% more compressed than JMave and JMimproved, respectively. Moreover, they produce comparable classification results, with an overall classification accuracy of 90.20 ± 0.19% and a Kappa coefficient of 88.80 ± 0.22%. However, different methods had their own advantages for the classification of different land classes. Specifically, JMave has a better classification only in cropland, while JMmc is advantageous for recognizing water bodies, tidal flats, and aquaculture. While JMimproved failed to retain vegetation and mangrove features, it enables a better depiction of the mangroves, salt pans, and vegetation classe...