Urban density mapping of global megacities from polarimetric SAR images

Abstract

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.

Analysis of Scattering Components From Fully Polarimetric Sar Images for Improving Accuracies of Urban Density Estimation

“…(a) T P and Pv+c, and (b) Ps and P d . Here, "hetero" and "homo" denote areas having heterogeneous and homogeneous POA distributions, respectively (Susaki et al, 2014). Table 2 are informative for this purpose.…”

“…We have reported a method for estimating urban density that uses an index Tv+c obtained by normalizing the sum of volume and helix scatterings Pv+c. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b;Susaki et al, 2014). While the method is found to be effective for estimating urban density, it is not clear why Tv+c is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas.…”

“…The method utilizes volumetric scattering derived from four-component decomposition methods (Yamaguchi et al, 2011). In addition, a method was presented that estimates urban density from a fully PolSAR image (Kajimoto and Susaki, 2013b;Susaki et al, 2014). The method uses an index Tv+c obtained by normalizing the sum of volume and helix scatterings Pv+c.…”

Analysis of Scattering Components From Fully Polarimetric Sar Images for Improving Accuracies of Urban Density Estimation

“…A previous study (Susaki et al, 2014) used fully polarimetric ALOS/PALSAR level 1.1 (L1.1) data. In this paper, we report the results of using data for New York City.…”

Urban Area Extraction Using Airborne X-Band Fully Polarimetric Pi-Sar2 Imagery

“…In previous researches, we have already reported a method to extract urban areas by using Advanced Land Observing Satellite (ALOS) / Phased Array type L-band Synthetic Aperture Rader (PALSAR) imagery (Kajimoto and Susaki, 2013a) and another method to estimate urban densities by using a single fully porlarimetric image (Kajimoto and Susaki, 2013b;Susaki et al, 2014). These supervised methods assume to use L-band fully polarimetric SAR (PolSAR) images, but it is not guaranteed that they perform against X-band PolSAR images.…”