The spatial and spectral heterogeneity of urban areas makes land cover classification a challenging process. In this study, we highlight the potential of combined multi-spectral Sentinel-2 and fully polarimetric PALSAR-2 data for land cover classification in dense urban areas, based on the Local Climate Zone (LCZ) scheme. We classified differently combined spectral and back-scattering characteristics using the subspace method in comparison with the Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) methods. Results show that, (i) the overall accuracy (OA) was 65.9% for the Sentinel-2 data, (ii) higher OA (71.9%) was achieved by adding four intensity images of PALSAR-2 to Sentinel-2, (iii) the inclusion of decomposed components increased OA to 72.8%, and (iv) the highest OA (73.3%) was achieved using all features. These results suggest that the inclusion of different backscattering characteristics disproportionately improved classification accuracy from using multi-spectral data alone. The results of comparison between different methods show that the subspace method performed better than SVM and MLC, particularly when high-dimensional data were used. The subspace method classified particularly well for some specific LCZ classes which are easily mixed between each other. It provides a promising option for LCZ mapping.
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