In this letter, an algorithm is proposed that robustly extracts urban areas from polarimetric synthetic aperture radar images. Polarization orientation angle (POA), volume scattering power (Pv) derived by four-component decomposition, and total power (TP) are utilized in the proposed algorithm. The dependence of the four decomposition components on POA can be lessened by rotating the elements of the coherency matrix by the POA. However, a level of POA dependence remains even after the correction. The proposed algorithm utilizes POA-corrected components, but pixels are grouped into several categories according to POA. First, urban and farmland training data are selected for each category in a study area. Then, urban and mountain areas are separated from farmland, bare ground, and sea by utilizing the Pv-TP scattergram. Finally, a measure of the POA randomness between neighboring pixels is used to discriminate between urban areas with nearly homogeneous POA and mountain areas with randomly distributed POAs. When performing classification on more than one study area, thresholds manually selected for one of the study areas are used to automatically estimate thresholds for the other areas. An accuracy assessment demonstrates that POA-based categorization and utilization of POA randomness contribute to improving classification accuracy.Index Terms-Four-component decomposition, polarimetric synthetic aperture radar (SAR), polarization orientation angle (POA), urban-area extraction.

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.

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.

In this paper, an urban density estimation algorithm from polarimetric synthetic aperture radar (SAR) images is proposed. Polarization orientation angle (POA) and four power components derived by four-component decomposition are utilized in the proposed algorithm. The dependence of the four decomposition components on POA can be lessened by rotating the elements of the coherency matrix by the POA. However, a level of POA dependence remains even after the correction. The proposed algorithm utilizes POA-corrected components, but pixels are grouped into several categories according to POA. Then, each component's scattering intensity is normalized for all pixels in each POA space. Finally, both the normalized intensity of volume scattering power (Pv) and that of the subtraction of surface scattering power (Ps) from double bounce scattering power (Pd) are combined to estimate urban density.

In this paper, an algorithm for urban mapping using building density from polarimetric synthetic aperture radar (PolSAR) images is proposed. Urban density estimation method applied in this study utilizes normalized combination of volume scattering power (Pv) and helix scattering power (Pc) derived by four-component decomposition method. However, this estimation method compresses the range of the scattering intensity between 0 and 1 because of normalization which is a part of POA correction process. The estimated index shows relative urban density which represents relative height of density within an image. Even if the values of estimated density are the same between different areas, they don't always indicate the same level of actual density. Thus, in comparison of urban density between different cities, absolute scale of urban density corresponding to actual building density of each area is indispensable. The main part of this paper is estimating absolute urban density using polarimetric indices. In this study, T11, T22, and T33 which are the diagonal elements of the coherency matrix are used. Each of them represents different scattering process. Finally, urban mapping using the estimated absolute density over various cities is conducted.