Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

Abstract

Digital surface model (DSMs) are sets of elevation data of the Earth's surface, useful for applications such as urban studies and height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric or tomographic configuration. Each image acquisition is usually focused in radar geometry. In this work, we present steps required to derive DSMs from SAR single-look complex (SLC) products focused in map geometry (geocoded). We modified existing tomographic reconstruction techniques to be able to operate with geocoded SLCs and extended methods to operate with 3D geocoded SLCs. The performance analysis showed that methods using 3D geocoded SLC products yielded DSMs with fewer outliers and retained more information of the illuminated area, with a cost of higher computational complexity. Compressive sensing methods using 2D geocoded SLCs can be a good alternative due to their comparatively moderate computational complexity. Index Terms-Synthetic aperture radar (SAR), tomography, urban, digital surface model (DSM).

Applications and level-2 products with the dual-baseline and linear-dual polarimetric MIRANDA35 airborne SAR system

“…For this purpose, we applied the procedure in Ref. 7. In this work we used a digital terrain model (DTM) to reduce computation time.…”

“…Pulse compression in elevation was performed with a single-dimensional search-based maximum likelihood estimation method as described in Ref. 7. The complex-valued correlation coefficients were computed by multilooking, combined with bilateral filtering Ref.…”