Building 3-D Reconstruction With a Small Data Stack Using SAR Tomography

Abstract

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well established for the 3-D reconstruction of urban buildings. Many methods have been proposed in the literature for TomoSAR inversion. These methods usually require fairly large data stacks (more than 20 images) for reliable reconstruction. Hence, they cannot be applied to the 3-D reconstruction of urban buildings using Gaofen-3 (GF-3) data directly, because there are few images available on average in each city. This article proposes a novel workflow for the 3-D reconstruction of high-rise buildings using small data stacks. In this workflow, we combine the methods of contour line extraction (CLE) and reference-elevation multilooking relaxation (RM-RELAX) for the 3-D reconstruction of high-rise buildings. The CLE method extracts contour lines of buildings relying on a data-driven approach and does not need to rely on any external data, which can provide prior knowledge for subsequent processing. The RM-RELAX method is an extension of RELAX, which can obtain more precise 3-D inversion with multilooking and reject outliers with the reference elevation (which can be obtained by the prior knowledge of contour lines) of each pixel. The applicability of this workflow is demonstrated by using simulated data and real data with six SAR images acquired by the GF-3 satellite over an area in Beijing, China.