3D Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Abstract

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.