CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: A PROOF OF CONCEPT OF ITERATIVE DSM IMPROVEMENT THROUGH SAR SCENE SIMULATION D. Derauw

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 A PROOF OF CONCEPT OF ITERATIVE DSM IMPROVEMENT THROUGH SAR SCENE SIMULATION D. Derauw Signal and Image Centre - Royal Military Academy, Renaissance Av., 1000 Brussels, Belgium - dderauw@elec.rma.ac.be Centre Spatial de Liège - Université de Liège, Avenue du Pré Aily, 4031 Angleur, Belgium - dderauw@ulg.ac.be KEY WORDS: SAR, Scene, Simulation, DEM, reconstruction ABSTRACT: In Very High Resolution (VHR) Synthesis Aperture Radar (SAR) context, very fine and accurate georeferencing and geoprojection processes are required. Both operations are only applicable if accurate local heights are known. 3D information may be derived from SAR interferometry (InSAR), But in VHR context, InSAR reveals to be inaccurate mostly due to phase unwrapping problems and to phase/height noise. Generated InSAR Digital Surface Models (DSM) can only be considered as a first good approximation of the observed surface. Therefore, we proposed to start from the InSAR DSM, to project it on ground range on a given datum, to model the observed scene using this projected DSM, then to simulate in slant range the intensity image issued from this structure model. Comparison between simulated and observed intensity image can then be used as a criterion to modify and improve the considered underlying DSM. In this paper, we present the different steps of the proposed approach and results obtained so far, showing that the proposed process can be run iteratively to modify the DSM and reach a stable solution. 1. INTRODUCTION A cooperation programme named ORFEO (Optic and Radar Federated Earth Observation) was set up between France and Italy to develop an Earth observation dual system, optic and radar, with metric resolution. Italy is in charge of the radar component (COSMO-Skymed), and France of the optic component (PLEIADES). Beside ORFEO, an accompanying programme was set-up to prepare the use and joint exploitation of images that will be provided from this satellites constellation. In the frame of this accompanying programme, the Belgian Science Policy (BelSPo) is financing the EMSOR project aiming at performing man-made object detection for urban map updating using VHR SAR and optical data. While such objective is well addressed in the optical imagery, this topic stays highly challenging in SAR imagery due to inherent peculiarities of SAR acquisition and imaging mode. Main obstacles are geometrical on one side and linked to SAR signal content on the other side. Geometrical deformation specific to SAR systems, i.e. layover, foreshortening, shadowing, make man-made structures appearing very differently in shape with respect to their appearance in optical imagery (Balz T. 2003). Specificities of SAR signal, mainly speckle, radar cross section dependence with incidence angle and multiple reflection processes make identical objects appear sufficiently differently to compromise, or make inoperative, classical detection techniques applicable in optical imagery. Man-made structures detection in SAR images based on speckle filtering followed by image segmentation is not applicable as such. Classification is often considered as a first processing step that, combined with other information layers, is used in higher level processing for fine Digital Surface Model (DSM) extraction and man-made structure detection (Tison et al. 2007, Thiele et al. 2007). SAR scene simulation was also proposed to help in fine georeferencing process (Blaz T. 2006) or to iteratively steer building structures detection and identification (Soerger et al., 2003). Similarly, in this paper, we propose an iterative way to improve a seed DSM that is obtained through classical Interferometric processing of single pass VHR SAR data. We developed a basic SAR intensity image simulator adapted to very high resolution. This one is then used to improve our seed DSM, comparing the simulated image in intensity with the detected one and using this comparison to perform blind DSM corrections without any a priori knowledge of the underlying urban structure. The proposed approach is justified by the fact that classical interferometric SAR (InSAR) is showing its limits in the VHR context. Therefore, on-ground projected InSAR DSM can be considered as a first approximation of the 3D observed surface and be used as a seed DSM to be improved. The main aim being man-made structure detection, improvement means here reaching a DSM representation allowing better detection and localisation of searched structures. This paper describes first results obtained and choices that have been made up to now to assess the validity of the proposed iterative process. Our first aim was to perform a proof of concept of the proposed approach, i.e. DSM improvement based on iterative comparison between a simulated and detected SAR intensity image. 2. TEST SITE AND SEED DSM 2.1 Data set description To generate our seed DSM, we are using a VHR InSAR pair acquired in February 2006 above Toulouse (France) by the RAMSES X-band sensor (Dupuis et al. 2000). Resolution cell dimensions are 0.55m in azimuth by 0.35m in slant range. We

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