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:: RAY TRACING AND SAR-TOMOGRAPHY FOR 3D ANALYSIS OF MICROWAVE SCATTERING AT MAN-MADE OBJECTS S. Auer, X. Zhu, S. Hinz, R. Bamler

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 158 2. SIMULATION CONCEPT The simulation approach presented in this paper is based on ray tracing algorithms provided by POV Ray (Persistence of Vision Ray Tracer), a free-ware ray tracing software. Main advantages of POV Ray are free access to its source code, optimized processing time, separability of multiple reflections and existing interfaces to common 3D model formats. In order to provide necessary output data for two-dimensional analysis of reflection phenomena, additional parts have been included to POV Ray’s source code. The simulation concept consists of four major parts: • Modeling of scene objects (Section 2.1) • Sampling of the 3D model scene in POV Ray (Section 2.2) • Creation of reflectivity maps (Section 2.3) • 3D analysis of reflection effects by means of output data provided by POV Ray (Section 2.4) In the following subsections, the processing chain will be explained in more detail. Cylindrical light Elevation Figure 1: Approximation of SAR system by a cylindrical light source and an orthographic camera; 3D sampling due to coordinates in azimuth, slant-range, and elevation 2.1 Modeling of scene objects First, the 3D scene to be illuminated by the virtual SAR sensor has to be described in the modeling step. 3D models can be designed in POV Ray or can be imported into the POV Ray environment. Then, parameters are adapted for describing the reflection behavior at object surfaces. To this end, POV Ray offers parametric models for specular reflection and diffuse reflection. A reflectivity factor for each surface defines the loss of intensity affecting rays specularly reflected at object surfaces. In the case of a modeled SAR system both the light source and the camera are located at the same position in space. The concept for approximating the imaging geometry of the SAR system is shown in Figure 1. Focusing effects due to SAR processing in azimuth and range are considered by using a cylindrical light source and an orthographic camera whose image plane is hit perpendicularly by incoming signals. 2.2 Sampling of the 3D model scene For analyzing backscattered signals within the modeled 3D scene, rays are followed in reverse direction starting at the center of an image pixel and ending at the ray’s origin at the light source (Whitted, 1980). This concept is commonly referred to as Backwards Ray Tracing (Glassner, 2002). Since ray tracing is performed for each pixel of the image plane, output data for creating reflectivity maps is derived by discrete sampling of the three-dimensional object scene (Auer et al., 2008). Coordinates in azimuth and range are derived by using depth information in slant-range provided during the sampling step. For instance, according to Figure 1, focused azimuth coordinates a, and slant-range coordinates r f of double bounce contributions are calculated by: a r , + a 0 p (1) r x +r^+ r 3 (2) where d Q , a p = azimuth coordinates of the ray’s origin and the ray’s destination at the image plane 7j , r 2 , 7*3 = depth values derived while tracing the ray through the 3D model scene So far, only two axes of the three-dimensional imaging system - azimuth and range - have been used for reflection analysis (Auer et al., 2008). However, the third dimension, elevation, may provide potential to enhance the simulators capacities to 3D analysis of reflection effects. To this end, extraction of elevation data has been added to the sampling step. According to the imaging concept shown in Figure 1, the elevation coordinate for a double bounce contribution is derived by means of the following equation: */ = e n +e 0 p (3) where e 0 , e p = elevation coordinates of the ray’s origin and the ray’s destination at the image plane At this point, elevation data derived during the sampling step shall be discussed in more detail. Due to Eq. (3) and the discrete sampling of the scene, all backscattering objects are assumed to behave as point scatterers. Resolution in elevation is not affected by limits occurring due to the size of sampling intervals along the elevation direction or the length of the elevation aperture (Nannini et al., 2008). From a physical point of view, deriving discrete points directly in elevation direction may be a disadvantage since comparison of the processed reflectivity function with a simulated one could be a desirable task. For instance, in the case of single bounce, the discrete concept will not be able to represent a planar surface continuously but only by discrete points. For layover caused by multiple reflections along the elevation direction the discrete simulation concept is nonetheless reasonable since approaches for tomographic analysis also seek for scatterers whose backscattered intensity is concentrated in individual points along the elevation direction. Concentration on scene and SAR geometry and thereby neglecting the physical characteristics provides some advantages, though, to overcome well known limitations of tomographic analysis (Zhu et al., 2008). For instance, it leads to a better understanding of the SAR geometry in the elevation direction by means of simulating the reflectivity slice which is helpful for 3D reconstruction. Additionally, it has the potential to provide the number of scatterers in a cell as a priori for parametric tomographic estimators if the scene geometry is available at a very detailed level, e.g. based on airborne LIDAR surface models.

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