BUILDING RECONSTRUCTION BASED ON A SMALL NUMBER OF TRACKS USING NONPARAMETRIC SAR TOMOGRAPHIC METHODS

Abstract. Nowadays, the synthetic aperture radar (SAR) tomography (TomoSAR) technique plays a notable role in 3D reconstruction of urban buildings through several SAR acquisitions with slightly different positions. Nonparametric-based TomoSAR spectral estimation algorithms usually work well when large number observations. In this study, limited images, we have assessed efficiency nonparametric methods, including maximum entropy (ME), singular value decomposition (SVD), linear prediction (LP), Capon, minimum norm (MN), and beamforming (BF) third dimension buildings. The experiments are conducted on both simulated TerraSAR-X stripmap images to indicate effectiveness LP proposed algorithm. analysis results proves that by minimizing average output signal power over antenna array elements, achieves discrimination distinct scatterers inside an image pixel. addition, low computational estimator improves sidelobe suppression height estimates complex multiple-scattering environment. Compared SVD, entropy, norm, beamforming, Eskan tower Tehran, Iran, obtained technique, is considerably near field-based measurement.