Urban Density Estimation From Polarimetric SAR Images Based on a POA Correction Method

Abstract

In this paper, an algorithm for estimating urban density from polarimetric synthetic aperture radar (SAR) images is proposed. Polarization orientation angle (POA) and four power components derived by four-component decomposition are used in the algorithm. In particular, in urban areas, SAR data are generally affected by factors such as the interval between buildings, building height, and building azimuth angle. Here, building azimuth (orientation) angle means the relative azimuth between the wall normal and the radar's ground range direction. The interval between buildings and building height are used for building density calculation such as the building-to-land ratio and the floor area ratio. However, building azimuth angle which depends on satellite orbit has almost no relation with building density. The scattering intensity of microwaves emitted from SAR has a strong dependence on this building azimuth angle. Therefore, the main part of this paper is focused on the correction of this angular effect. The first step in the POA correction method is the extraction of homogeneous-POA city districts. In the second step each power component's scattering intensity is normalized for all pixels in a particular POA interval separately for different POA types of districts. In the case of Tokyo metropolitan area, Japan, estimated urban density from ALOS/PALSAR data has correlation coefficients of nearly 0.7 with the building-to-land ratio and 0.5 with the floor area ratio on the scale of hundreds of meter. In the areas where strong POA dependence is seen, the improvement of the correlation coefficient runs up to approximately 0.2.

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

“…Ferro et al (2011) studied the relationship between the double-bounce effect of buildings and the orientation angles in VHR SAR images. Kajimoto and Susaki (2013b) also reported the experimental results in an anechoic room and the total powers of backscattering and the four components (Yamaguchi et al, * Corresponding author. 2005) derived from fully polarimetric scattering are deeply dependent on the orientation angles. This issue is quite critical in extracting urban areas and buildings in urban areas.…”

“…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.…”

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

“…Then, we applied a four component decomposition method that corrects POA angle effects (Yamaguchi et al, 2011). It was reported that a normalized combination of the volume scattering power and the helix scattering power (Tv+c) shows the best correlation with both the building-to-land ratio and the floor area ratio (Equation (1) (Kajimoto and Susaki, 2013b). The normalization uses mean and standard deviation of the volume scattering power and the helix scattering power (Pv+c).…”

“…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.…”