Improving Landsat ETM+ Urban Area Mapping via Spatial and Angular Fusion With MISR Multi-Angle Observations

Urban landscapes are a complex combination of buildings, roads, vegetation, soil, and water, each of which exhibits unique radiative and thermal properties. To understand the dynamics of patterns and processes and their interactions in heterogeneous landscapes such as urban areas, more precise urban mapping techniques are of essential importance. Several investigations have demonstrated that Bidirectional Reflectance Distribution Function (BRDF) information can be utilized to complement spectral information to improve land cover (especially vegetation) classification accuracies on the local, regional and global scales. However, the potential benefits of adding remotely sensed angular information to improve urban mapping have rarely been explored. This paper uses Multi-angle Imaging SpectroRadiometer (MISR) data to investigate the view angle effects on spectral response and discrimination of urban land cover types in Shenzhen, China. For this purpose, a spatial and angular fusion (SAF) model was developed for blending MISR and Enhanced Thematic Mapper Plus (ETM+) images. A classification of the fused data with twenty channels using support vector machines (SVM) and a post-classification probability relaxation were then performed after channel selection through principal-component analysis (PCA). The results showed that the contribution of MISR to improving ETM+ urban mapping accuracy was 2.86% in our experiments and its statistical significance was validated by McNemar´s test.