| 摘 要： |
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 (JM(ave)), J-M distance based on weights and correlations (JM(improved)), and heuristic J-M distance (JM(mc)). 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 JM(ave), JM(improved), and JM(mc) methods. The results show that the three methods compress feature dimensions and retain all feature types as much as possible. JM(mc) exhibits the most significant compression from a value of 35 to 15 (57.14%), which is 37.14% and 40% more compressed than JM(ave) and JM(improved), respectively. Moreover, they produce comparable classification results, with an overall classification accuracy of 90.20 & PLUSMN; 0.19% and a Kappa coefficient of 88.80 & PLUSMN; 0.22%. However, different methods had their own advantages for the classification of different land classes. Specifically, JM(ave) has a better classification only in cropland, while JM(mc) is advantageous for recognizing water bodies, tidal flats, and aquaculture. While JM(improved) failed to retain vegetation and mangrove features, it enables a better depiction of the mangroves, salt pans, and vegetation classes. Both JM(ave) and JM(improved) rearrange features based on J-M distance, while JM(mc) places more emphasis on feature selection. As a result, there can be significant differences in feature subsets among these three methods. Therefore, the comparative analysis of these three methods further elucidates the importance of J-M distance in feature selection, demonstrating the significant potential of J-M distance-based feature selection methods in wetland classification. |
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