Abstract
Abstract. The interest for the radargrammetric approach to Digital Surface Models (DSMs) generation has been growing in last years thanks to the availability of very high resolution imagery acquired by new SAR (Synthetic Aperture Radar) sensors, as COSMO-SkyMed, Radarsat-2 and TerraSAR-X, which are able to supply imagery up to 1 m ground resolution. DSMs radargrammetric generation approach consists of two basic steps, as for the standard photogrammetry applied to optical imagery: the imagery (at least a stereo pair) orientation and the image matching for the generation of the points cloud. The steps of the radargrammetric DSMs generation have been implemented into SISAR (Software per Immagini Satellitari ad Alta Risoluzione), a scientific software developed at Geodesy and Geomatics Institute of the University of Rome “La Sapienza”. Moreover, starting from the radargrammetric orientation model, a tool for the Rational Polynomial Coefficients (RPCs) for SAR images have been implemented. The possibility to generate RPCs, re-parametrizing a rigorous orientation model through a standardized set of coefficients which can be managed by a Rational Polynomial Coefficients (RPFs) model (similarly to optical high resolution imagery) sounds of particular interest since, at present, the most part of SAR imagery (except from Radarsat-2) is not supplied with RPCs, although the corresponding RPFs model is available in several commercial software. In particular the RPCs model has been used in the matching process and in the stereo restitution for the DSMs generation, with the advantage of shorter computational time. This paper discusses the application and the results of the implemented algorithm for radargrammetric DSMs generation from TerraSAR-X SpotLight imagery, acquired in Spotlight mode over Trento (Northern Italy). Urban and extra-urban (forested, cultivated) areas were considered in two different tiles, and a final overall accuracy ranging from 4.5 to 6 meters was achieved as regards the point clouds, enough well distributed independently from the land cover; moreover, it was highlighted the benefit to filter the originally derived points cloud with a global DSM as SRTM DEM, what leads to an accuracy improvement of about 20% paying a loss of matched points of about 10 %.
Highlights
Both optical and SAR imagery are characterized by proper deformations due to the different acquisition geometries and processes, which have to be duly taken into account during the two fundamental steps for Digital Surface Models (DSMs) generation if their potentialities have to be fully exploited
Paragraphs 2, 3 and 4 present the fundamentals of the DSMs generation procedure, mainly focusing on the orientation models, the pre-processing denoising strategy and the matching methodology; paragraph 5 discusses the results related to the processed images and the conclusions are drawn in paragraph 6
The use of the Rational Polynomial Coefficients (RPFs) of model is common in several commercial software, at least for three important reasons: the implementation of the RPFs model is standard, unique for all the sensors and much more simple that the one of a rigorous model, which have to be customized for each sensor; the performances of the RPFs model can be at the level of the ones from rigorous models; the usage requires zero or, at maximum, quite few Ground Control Points (GCPs), if additional refinement transformations are used
Summary
Both optical and SAR imagery are characterized by proper deformations due to the different acquisition geometries and processes, which have to be duly taken into account during the two fundamental steps for DSMs generation (image orientation and matching) if their potentialities have to be fully exploited. As regards the image matching, different approaches (mainly regarding optical imagery) have been developed primarily in the aerial photogrammetry, like the classical Area Based Matching (ABM), or the Feature Based Matching (FBM) (Zhang and Gruen, 2006), or those based on cost functions and dynamic programming techniques (Birchfield and Tomasi, 1999), or the recent and quite promising semi-global matching technique (Hirschmüller, 2008) Some of these approaches, already well established for the aerial and satellite optical imagery, have been recently adapted to SAR imagery for the radargrammetric DSMs generation, and at present they are implemented in several commercial software; basically they were tuned over low resolution SAR imagery. Paragraphs 2, 3 and 4 present the fundamentals of the DSMs generation procedure, mainly focusing on the orientation models (rigorous and RPFs), the pre-processing denoising strategy and the matching methodology; paragraph 5 discusses the results related to the processed images and the conclusions are drawn in paragraph 6
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