Maximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas |
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| Institution: | Photogrammetry and Remote Sensing, Technische Universitaet Muenchen (TUM), Arcisstr. 21, 80333 Munich, Germany;Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, China;College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China;UNC Chapel Hill, United States;Science and Technology on Multi-spectral Information Processing Laboratory, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, PR China;Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, Netherlands;Dept. of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA;Universite de Caen Basse Normandie, ENSICAEN, GREYC - UMR CNRS 6972, 6.Bvd Marechal Juin, 14050 Caen, France |
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| Abstract: | The reconstruction of digital surface models (DSMs) of urban areas from interferometric synthetic aperture radar (SAR) data is a challenging task. In particular the SAR inherent layover and shadowing effects need to be coped with by sophisticated processing strategies. In this paper, a maximum-likelihood estimation procedure for the reconstruction of DSMs from multi-aspect multi-baseline InSAR imagery is proposed. In this framework, redundant as well as contradicting observations are exploited in a statistically optimal way. The presented method, which is especially suited for single-pass SAR interferometers, is examined using test data consisting of experimental airborne millimeterwave SAR imagery. The achievable accuracy is evaluated by comparison to LiDAR-derived reference data. It is shown that the proposed estimation procedure performs better than a comparable non-statistical reconstruction method. |
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| Keywords: | Synthetic Aperture Radar (SAR) Multi-aspect Multi-baseline Airborne SAR interferometry (InSAR) Maximum likelihood estimation |
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