Urban density mapping of global megacities from polarimetric SAR images

We propose an algorithm for estimating urban density from polarimetric synthetic aperture radar (SAR) images, and compare the urban density patterns of global megacities. SAR images are uniquely able to detect structural information of objects, but they are very sensitive to orientation angle. This issue has been an obstacle to applying SAR images to urban areas. Kajimoto and Susaki (2013b) proposed an algorithm to handle this issue. The effects of polarization orientation angle (POA) are removed by rotating the coherency matrix and then calculating the mean and standard deviation of scattering power by POA domain. The algorithm can estimate urban density from a single fully polarimetric SAR image but has the drawback that the generated urban density maps of multiple images are not comparable with each other because the algorithm generates a relative urban density valid only within the analyzed image. We therefore extend the method by calculating POA-domain statistics from all images of interest so that the generated maps can be compared. Estimated urban densities are assessed on two types of urban density generated from GIS data, building-to-land ratio and floor-area ratio. We demonstrate that the extended method can estimate urban density with reasonable accuracy. Finally, we generate two scattergrams of indices derived from urban density maps of global megacities. An analysis using the scattergrams indicates insightful information about the patterns of urban development. We conclude that the proposed algorithm and the analysis using the obtained results are beneficial to understanding the conditions in megacities.