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.

城市地区的空间和光谱异质性使土地覆盖分类成为一个具有挑战性的过程。在这项研究中，我们重点介绍了基于本地气候区（LCZ）方案的多光谱Sentinel-2和全极化PALSAR-2数据在稠密城市地区土地覆盖分类中的潜力。与支持向量机（SVM）和最大似然分类器（MLC）方法相比，我们使用子空间方法对不同组合的光谱和反向散射特性进行了分类。结果表明，（i）Sentinel-2数据的总体准确度（OA）为65.9％，（ii）通过在Sentinel-2中添加四个强度图像PALSAR-2获得了更高的OA（71.9％），（iii ）包含分解的成分将OA增加到72.8％，并且（iv）使用所有功能均获得了最高的OA（73.3％）。这些结果表明，与单独使用多光谱数据相比，包含不同的反向散射特性会极大地提高分类精度。不同方法之间的比较结果表明，子空间方法的性能优于SVM和MLC，尤其是在使用高维数据时。对于某些易于相互混合的特定LCZ类，子空间方法的分类特别好。它为LCZ映射提供了一个有前途的选择。对于某些易于相互混合的特定LCZ类，子空间方法的分类特别好。它为LCZ映射提供了一个有前途的选择。对于某些易于相互混合的特定LCZ类，子空间方法的分类特别好。它为LCZ映射提供了一个有前途的选择。