SVM-based soft classification of urban tree species using very high-spatial resolution remote-sensing imagery

Classification from remote-sensing data has proved to be fast and effective for mapping tree species. However, the interference from background noise and the weak spectral separability among most tree species have negative impacts on classification accuracy and may make conventional classification methods inappropriate. To solve this problem, a soft classification approach is presented. It consists of the technologies of soft partition, defuzzifying, and feature-location analyses (referred to here as F-L analyses). The soft partition is conducted by combined binary support vector machines, enabling a natural prototype of fuzzy clusters to be formed. Unsure members (members means both patches and pixels) in the clustering prototype can be defuzzified afterwards. F-L analyses run through the entire process in two ways: (1) by adding density description to feature space for classification and (2) through density-constrained defuzzifying. The former can enrich the feature space, thus making the soft partition perform better. By using the latter, the credibility of a class label is indicated by several weighted fuzzy measures in the form of hierarchical densities, thereby reducing the uncertainty of the defuzzifying. Experiments indicate that only 63% of members on average are surely allocated in a clustering prototype and the remainder can be ensured by the explored defuzzifying approach. The mean overall accuracy (OA) of the defuzzified members is very close to that of the originally allocated sure ones. A comparative assessment shows that the mean OA and mean kappa statistic value of members defuzzified using the explored fuzzy measure are, on average, 3.84 and 4.89% respectively higher than those using conventional maximum membership measures. Experiments also show that the former is superior to the latter in regard to the formation of complete tree crown objects.