Radar Remote Sensing Of Urban Areas - Dr. Muzaffer Can İban
Radar Remote Sensing of Urban Areas
Remote Sensing and Digital Image ProcessingVOLUME 15Series Editor:EARSel Series Editor:Freek D. van der MeerDepartment of Earth Systems AnalysisInternational Instituite forGeo-Information Science andEarth Observation (ITC)Enchede, The Netherlands&Department of Physical GeographyFaculty of GeosciencesUtrecht UniversityThe NetherlandsAndré MarçalDepartment of Applied MathematicsFaculty of SciencesUniversity of PortoPorto, PortugalEditorial Advisory Board:EARSel Editorial Advisory Board:Michael AbramsNASA Jet Propulsion LaboratoryPasadena, CA, U.S.A.Mario A. GomarascaCNR - IREA Milan, ItalyPaul CurranUniversity of Bournemouth, U.K.Arnold DekkerCSIRO, Land and Water DivisionCanberra, AustraliaMartti HallikainenHelsinki University of TechnologyFinlandHåkan OlssonSwedish Universityof Agricultural SciencesSwedenSteven M. de JongDepartment of Physical GeographyFaculty of GeosciencesUtrecht University, The NetherlandsEberhard ParlowUniversity of BaselSwitzerlandMichael SchaepmanDepartment of GeographyUniversity of Zurich, SwitzerlandRainer ReuterUniversity of OldenburgGermanyFor other titles published in this series, go tohttp://www.springer.com/series/6477
Radar Remote Sensingof Urban AreasUwe SoergelEditorLeibniz Universität HannoverInstitute of Photogrammetry and GeoInformation, Germany123
EditorUwe SoergelLeibniz Universität HannoverInstitute of Photogrammetry and GeoInformationNienburger Str. 130167 HannoverGermanysoergel@ipi.uni-hannover.deCover illustration: Fig. 7 in Chapter 11 in this bookResponsible Series Editor: André MarçalISSN 1567-3200ISBN 978-90-481-3750-3e-ISBN 978-90-481-3751-0DOI 10.1007/978-90-481-3751-0Springer Dordrecht Heidelberg London New YorkLibrary of Congress Control Number: 2010922878c Springer Science Business Media B.V. 2010 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or byany means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without writtenpermission from the Publisher, with the exception of any material supplied specifically for the purposeof being entered and executed on a computer system, for exclusive use by the purchaser of the work.Cover design: deblik, BerlinPrinted on acid-free paperSpringer is part of Springer Science Business Media (www.springer.com)
PrefaceOne of the key milestones of radar remote sensing for civil applications was thelaunch of the European Remote Sensing Satellite 1 (ERS 1) in 1991. The platformcarried a variety of sensors; the Synthetic Aperture Radar (SAR) is widely considered to be the most important. This active sensing technique provides all-day andall-weather mapping capability of considerably fine spatial resolution. ERS 1 andits sister system ERS 2 (launch 1995) were primarily designed for ocean applications, but soon the focus of attention turned to onshore mapping. Examples fortypical applications are land cover classification also in tropical zones and monitoring of glaciers or urban growth. In parallel, international Space Shuttle Missionsdedicated to radar remote sensing were conducted starting already in the 1980s.The most prominent were the SIR-C/X-SAR mission focussing on the investigationof multi-frequency and multi-polarization SAR data and the famous Shuttle RadarTopography Mission (SRTM). Data acquired during the latter enabled to derive aDEM of almost global coverage by means of SAR Interferometry. It is indispensable even today and for many regions the best elevation model available. DifferentialSAR Interferometry based on time series of imagery of the ERS satellites and theirsuccessor Envisat became an important and unique technique for surface deformation monitoring.The spatial resolution of those devices is in the order of some tens of meters.Image interpretation from such data is usually restricted to radiometric properties,which limits the characterization of urban scenes to rather general categories, forexample, the discrimination of suburban areas from city cores. The advent of a newsensor generation changed this situation fundamentally. Systems like TerraSAR-X(Germany) and COSMO-SkyMed (Italy) achieve geometric resolution of about 1 m.In addition, these sophisticated systems are more agile and provide several modestailored for specific tasks. This offers the opportunity to extend the analysis toindividual urban objects and their geometrical set-up, for instance, infrastructureelements like roads and bridges, as well as buildings. In this book, potentials andlimits of SAR for urban mapping are described, including SAR Polarimetry andSAR Interferometry. Applications addressed comprise rapid mapping in case of timecritical events, road detection, traffic monitoring, fusion, building reconstruction,SAR image simulation, and deformation monitoring.v
viPrefaceAudienceThis book is intended to provide a comprehensive overview of the state-of-the artof urban mapping and monitoring by modern satellite and airborne SAR sensors.The reader is assumed to have a background in geosciences or engineering andto be familiar with remote sensing concepts. Basics of SAR and an overview ofdifferent techniques and applications are given in Chapter 1. All chapters followingthereafter focus on certain applications, which are presented in great detail by wellknown experts in these fields.In case of natural disaster or political crisis rapid mapping is a key issue(Chapter 2). An approach for automated extraction of roads and entire road networks is presented in Chapter 3. A topic closely related to road extraction is trafficmonitoring. In case of SAR, Along-Track Interferometry is a promising techniquefor this task, which is discussed in Chapter 4. Reflections at surface boundariesmay alter the polarization plane of the signal. In Chapter 5, this effect is exploitedfor object recognition from a set of SAR images of different polarization states attransmit and receive. Often, up-to-date SAR data has to be compared with archivedimagery of complementing spectral domains. A method for fusion of SAR and optical images aiming at classification of settlements is described in Chapter 6. Theopportunity to determine the object height above ground from SAR Interferometryis of course attractive for building recognition. Approaches designed for monoaspect and multi-aspect SAR data are proposed in Chapters 7 and 8, respectively.Such methods may benefit from image simulation techniques that are also usefulfor education. In Chapter 9, a methodology optimized for real-time requirements ispresented. Monitoring of surface deformation suffers from temporal signal decorrelation especially in vegetated areas. However, in cities many temporally persistentscattering objects are present, which allow tracking of deformation processes evenfor periods of several years. This technique is discussed in Chapter 10. Finally, inChapter 11, design constraints of a modern airborne SAR sensor are discussed forthe case of an existing device together with examples of high-quality imagery thatstate-of-the-art systems can provide.Uwe Soergel
Contents1Review of Radar Remote Sensing on Urban Areas . . . . . . . . . . . . . . . . . . . . . .Uwe Soergel1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.2 Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.2.1 Imaging Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.2.2 Mapping of 3d Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3 2d Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.1 Pre-processing and Segmentation of Primitive Objects. . . . .1.3.2 Classification of Single Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.2.1 Detection of Settlements. . . . . . . . . . . . . . . . . . . . . . . . .1.3.2.2 Characterization of Settlements . . . . . . . . . . . . . . . . .1.3.3 Classification of Time-Series of Images . . . . . . . . . . . . . . . . . . .1.3.4 Road Extraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.4.1 Recognition of Roads and of Road Networks . . .1.3.4.2 Benefit of Multi-aspect SARImages for Road Network Extraction . . . . . . . . . . .1.3.5 Detection of Individual Buildings . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.6 SAR Polarimetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.6.1 Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.6.2 SAR Polarimetry for Urban Analysis . . . . . . . . . . .1.3.7 Fusion of SAR Images with Complementing Data . . . . . . . . .1.3.7.1 Image Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.3.7.2 Fusion for Land Cover Classification . . . . . . . . . . .1.3.7.3 Feature-Based Fusion ofHigh-Resolution Data. . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4 3d Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.1 Radargrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.1.1 Single Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.1.2 Stereo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.1.3 Image Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
viiiContents1.4.223SAR Interferometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.2.1 InSAR Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.2.2 Analysis of a Single SAR Interferogram . . . . . . . .1.4.2.3 Multi-image SAR Interferometry . . . . . . . . . . . . . . .1.4.2.4 Multi-aspect InSAR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.3 Fusion of InSAR Data and Other RemoteSensing Imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4.4 SAR Polarimetry and Interferometry . . . . . . . . . . . . . . . . . . . . . . .1.5 Surface Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.5.1 Differential SAR Interferometry . . . . . . . . . . . . . . . . . . . . . . . . . . .1.5.2 Persistent Scatterer Interferometry. . . . . . . . . . . . . . . . . . . . . . . . .1.6 Moving Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2929323434Rapid Mapping Using Airborne and Satellite SAR Images . . . . . . . . . . . .Fabio Dell’Acqua and Paolo Gamba2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.2 An Example Procedure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.2.1 Pre-processing of the SAR Images . . . . . . . . . . . . . . . . . . . . . . . . .2.2.2 Extraction of Water Bodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.2.3 Extraction of Human Settlements . . . . . . . . . . . . . . . . . . . . . . . . . .2.2.4 Extraction of the Road Network . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.2.5 Extraction of Vegetated Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.2.6 Other Scene Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.3 Examples on Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.3.1 The Chengdu Case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.3.2 The Luojiang Case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49Feature Fusion Based on Bayesian Network Theoryfor Automatic Road Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Uwe Stilla and Karin Hedman3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.2 Bayesian Network Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.3 Structure of a Bayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.3.1 Estimating Continuous ConditionalProbability Density Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.3.2 Discrete Conditional Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . .3.3.3 Estimating the A-Priori Term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.5 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67980818285
Contentsix4Traffic Data Collection with TerraSAR-Xand Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Stefan Hinz, Steffen Suchandt, Diana Weihing,and Franz Kurz4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874.2 SAR Imaging of Stationary and Moving Objects . . . . . . . . . . . . . . . . . . . . 884.3 Detection of Moving Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 934.3.1 Detection Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.3.2 Integration of Multi-temporal Data . . . . . . . . . . . . . . . . . . . . . . . . . 964.4 Matching Moving Vehicles in SAR and Optical Data . . . . . . . . . . . . . . . 984.4.1 Matching Static Scenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984.4.2 Temporal Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1004.5 Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1014.5.1 Accuracy of Reference Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1014.5.2 Accuracy of Vehicle Measurements in SAR Images . . . . . . .1034.5.3 Results of Traffic Data Collectionwith TerraSAR-X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034.6 Summary and Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1075Object Recognition from Polarimetric SAR Images . . . . . . . . . . . . . . . . . . . . .109Ronny Hänsch and Olaf Hellwich5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1095.2 SAR Polarimetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1115.3 Features and Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1175.4 Object Recognition in PolSAR Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1245.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1306Fusion of Optical and SAR Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133Florence Tupin6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1336.2 Comparison of Optical and SAR Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . .1356.2.1 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1366.2.2 Geometrical Distortions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1376.3 SAR and Optical Data Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1386.3.1 Knowledge of the Sensor Parameters . . . . . . . . . . . . . . . . . . . . . .1386.3.2 Automatic Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1406.3.3 A Framework for SAR and Optical DataRegistration in Case of HR Urban Images . . . . . . . . . . . . . . . . .1416.3.3.1 Rigid Deformation Computationand Fourier–Mellin Invariant . . . . . . . . . . . . . . . . . . .1416.3.3.2 Polynomial Deformation . . . . . . . . . . . . . . . . . . . . . . . .1436.3.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .144
xContents6.4Fusion of SAR and Optical Data for Classification. . . . . . . . . . . . . . . . . .1446.4.1 State of the Art of Optical/SAR Fusion Methods . . . . . . . . . . .1446.4.2 A Framework for Building DetectionBased on the Fusion of Optical and SAR Features . . . . . . . . .1476.4.2.1 Method Principle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1476.4.2.2 Best Rectangular Shape Detection . . . . . . . . . . . . . .1486.4.2.3 Complex Shape Detection . . . . . . . . . . . . . . . . . . . . . .1496.4.2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1506.5 Joint Use of SAR Interferometry and Optical Datafor 3D Reconstruction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1516.5.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1516.5.2 Extension to the Pixel Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1546.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1577Estimation of Urban DSM from Mono-aspect InSARImages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .161Céline Tison and Florence Tupin7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1617.2 Review of Existing Methods for Urban DSM Estimation . . . . . . . . . . .1637.2.1 Shape from Shadow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1647.2.2 Approximation of Roofs by Planar Surfaces . . . . . . . . . . . . . . .1647.2.3 Stochast
One of the key milestones of radar remote sensing for civil applications was the launch of the European Remote Sensing Satellite 1 (ERS 1) in 1991. The platform carried a variety of sensors; the Synthetic Aperture Radar (SAR) is widely consid-ered to be the most impo
PRINCIPLES OF REMOTE SENSING Shefali Aggarwal Photogrammetry and Remote Sensing Division Indian Institute of Remote Sensing, Dehra Dun Abstract : Remote sensing is a technique to observe the earth surface or the atmosphere from out of space using satellites (space borne) or from the air using aircrafts (airborne). Remote sensing uses a part or several parts of the electromagnetic spectrum. It .
Scope of remote sensing Remote sensing: science or art? The remote sensing process Applications of remote sensing Information flow in remote sensing The EMRreflected, emitted, or back-scattered from an object or geographic area is used as a surrogatefor the actual property under investigation.
SYNTHETIC APERTURE RADAR (SAR) IMAGING BASICS 1.1 Basic Principles of Radar Imaging / 2 1.2 Radar Resolution / 6 1.3 Radar Equation /10 1.4 Real Aperture Radar /11 1.5 Synthetic Aperture Radar /13 1.6 Radar Image Artifacts and Noise / 16 1.6.1 Range and Azimuth Ambi
Remote Sensing Of Climate Change processes in the Earth system Summary Slide 9 2. In your study of modern remote sensing methods, be sure to learn about the following remote sensing processes listed in paragraph 3b of the 2022 event sheet. Active & passive sensors Optical & infrared imagers Radiometers LiDAR Radar altimetry Precipitation radar
Proximity Sensor Sensing object Reset distance Sensing distance Hysteresis OFF ON Output Proximity Sensor Sensing object Within range Outside of range ON t 1 t 2 OFF Proximity Sensor Sensing object Sensing area Output (Sensing distance) Standard sensing object 1 2 f 1 Non-metal M M 2M t 1 t 2 t 3 Proximity Sensor Output t 1 t 2 Sensing .
bistatic radar geometry.27 Radar-absorbent material augments fuselage shaping by absorbing radar energy and reducing the strength of the radar echo.28 Future innova-tions may allow stealth aircraft to actively cancel radar echo by retransmitting radar energy and/or by ionizing boundary layer air around the fuselage.29 Counters to Stealth
Urban Design is only is 85; there is no application fee. Further information and application form see the UDG website www.udg.org.uk or phone 020 7250 0892 Urban Degsi n groUp Urban U Degsi n groUp UrBan DesiGn145 Winter 2018 Urban Design Group Journal ISSN 1750 712X nortH aMeriCa URBAN DESIGN GROUP URBAN DESIGN
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