Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67％ and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75％ to 2.07％ compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88％ to 6.58％ lower than that of corresponding S-2 products in the groups.

Dang Hung Bui;László Mucsi

Department of Geoinformatics,Physical and Environmental Geography,University of Szeged,Szeged,Hungary;Institute for Environmental Science,Engineering and Management,Industrial University of Ho Chi Minh City,Ho Chi Minh City,Vietnam

地球空间信息科学学报（英文版）

[1]Dang Hung Bui;László Mucsi-.Comparison of Layer-stacking and Dempster-Shafer Theory-based Methods Using Sentinel-1 and Sentinel-2 Data Fusion in Urban Land Cover Mapping)[J].地球空间信息科学学报（英文版）,2022(03):425-438

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