C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page i 12.9.2007 3:20pm Compositor Name: JGanesan Image Processing for Remote Sensing
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page ii 12.9.2007 3:20pm Compositor Name: JGanesan
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page iii 12.9.2007 3:20pm Compositor Name: JGanesan Image Processing for Remote Sensing Edited by C. H. Chen Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page v 12.9.2007 3:20pm Compositor Name: JGanesanPrefaceThis volume is a spin-off edition derived from Signal and Image Processing for RemoteSensing. It presents more advanced topics of image processing in remote sensing thansimilar books in the area. The topics of image modeling, statistical image classifiers,change detection, independent component analysis, vertex component analysis, imagefusion for better classification or segmentation, 2-D time series modeling, neural networkclassifications, etc. are examined in this volume. Some unique topics like accuracy assess-ment and information-theoretic measure of multiband images are presented. An em-phasis is placed on the issues with synthetic aperture radar (SAR) images in manychapters. Continued development on imaging sensors always presents new opportunitiesand challenges on image processing for remote sensing. The hyperspectral imagingsensor is a good example here. We believe this volume not only presents the most up-to-date developments of image processing for remote sensing but also suggests to readersthe many challenging problems ahead for further study.Original Preface from Signal and Image Processing for Remote SensingBoth signal processing and image processing have been playing increasingly importantroles in remote sensing. While most data from satellites are in image forms and thusimage processing has been used most often, signal processing can contribute significantlyin extracting information from the remotely sensed waveforms or time series data. Incontrast to other books in this field which deal almost exclusively with the imageprocessing for remote sensing, this book provides a good balance between the roles ofsignal processing and image processing in remote sensing. The book covers mainlymethodologies of signal processing and image processing in remote sensing. Emphasisis thus placed on the mathematical techniques which we believe will be less changed ascompared to sensor, software and hardware technologies. Furthermore, the term ‘‘remotesensing’’ is not limited to the problems with data from satellite sensors. Other sensorswhich acquire data remotely are also considered. Thus another unique feature of the bookis the coverage of a broader scope of the remote sensing information processing problemsthan any other book in the area. The book is divided into two parts [now published as separate volumes under thefollowing titles]. Part I, Signal Processing for Remote Sensing, has 12 chapters and Part II[comprising the present volume], Image Processing for Remote Sensing, has 16 chapters. Thechapters are written by leaders in the field. We are very fortunate, for example, to haveDr. Norden Huang, inventor of the Huang–Hilbert transform, along with Dr. StevenLong, to write a chapter on the application of the transform to remote sensing problem,and Dr. Enders A. Robinson, who has made many major contributions to geophysicalsignal processing for over half a century, to write a chapter on the basic problem ofconstructing seismic images by ray tracing. In Part I, following Chapter 1 by Drs. Long and Huang, and my short Chapter 2 on theroles of statistical pattern recognition and statistical signal processing in remote sensing,we start from a very low end of the electromagnetic spectrum. Chapter 3 considers theclassification of infrasound at a frequency range of 0.001 Hz to 10 Hz by using a parallelbank neural network classifier and a 11-step feature selection process. The >90% correctclassification rate is impressive for this kind of remote sensing data. Chapter 4 through
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page vi 12.9.2007 3:20pm Compositor Name: JGanesanChapter 6 deal with seismic signal processing. Chapter 4 provides excellent physicalinsights on the steps for construction of digital seismic images. Even though the seismicimage is an image, this chapter is placed in Part I as seismic signals start as waveforms.Chapter 5 considers the singular value decomposition of a matrix data set from scalar-sensors arrays, which is followed by independent component analysis (ICA) step to relaxthe unjustified orthogonality constraint for the propagation vectors by imposing astronger constraint of fourth-order independence of the estimated waves. With an initialfocus of the use of ICA in seismic data and inspired by Dr. Robinson’s lecture on seismicdeconvolution at the 4th International Symposium, 2002, on Computer Aided SeismicAnalysis and Discrimination, Mr. Zhenhai Wang has examined approaches beyond ICAfor improving seismic images. Chapter 6 is an effort to show that factor analysis, as analternative to stacking, can play a useful role in removing some unwanted components inthe data and thereby enhancing the subsurface structure as shown in the seismic images.Chapter 7 on Kalman filtering for improving detection of landmines using electromag-netic signals, which experience severe interference, is another remote sensing problem ofhigher interest in recent years. Chapter 8 is a representative time series analysis problemon using meteorological and remote sensing indices to monitor vegetation moisturedynamics. Chapter 9 actually deals with the image data for digital elevation model butis placed in Part I mainly because the prediction error (PE) filter is originated from thegeophysical signal processing. The PE filter allows us to interpolate the missing parts ofan image. The only chapter that deals with the sonar data is Chapter 10, which shows thata simple blind source separation algorithm based on the second-order statistics can bevery effective to remove reverberations in active sonar data. Chapter 11 and Chapter 12are excellent examples of using neural networks for retrieval of physical parameters fromthe remote sensing data. Chapter 12 further provides a link between signal and imageprocessing as the principal component analysis and image sharpening tools employed areexactly what are needed in Part II. With a focus on image processing of remote sensing images, Part II begins with Chapter13 [Chapter 1 of the present volume] that is concerned with the physics and mathematicalalgorithms for determining the ocean surface parameters from synthetic aperture radar(SAR) images. Mathematically Markov random field (MRF) is one of the most usefulmodels for the rich contextual information in an image. Chapter 14 [now Chapter 2]provides a comprehensive treatment of MRF-based remote sensing image classification.Besides an overview of previous work, the chapter describes the methodological issuesinvolved and presents results of the application of the technique to the classification ofreal (both single-date and multitemporal) remote sensing images. Although there aremany studies on using an ensemble of classifiers to improve the overall classificationperformance, the random forest machine learning method for classification of hyperspec-tral and multisource data as presented in Chapter 15 [now Chapter 3] is an excellentexample of using new statistical approaches for improved classification with the remotesensing data. Chapter 16 [now Chapter 4] presents another machine learning method,AdaBoost, to obtain robustness property in the classifier. The chapter further considersthe relations among the contextual classifier, MRF-based methods, and spatial boosting.The following two chapters are concerned with different aspects of the change detectionproblem. Change detection is a uniquely important problem in remote sensing as theimages acquired at different times over the same geographical area can be used in theareas of environmental monitoring, damage management, and so on. After discussingchange detection methods for multitemporal SAR images, Chapter 17 [now Chapter 5]examines an adaptive scale–driven technique for change detection in medium resolu-tion SAR data. Chapter 18 [now Chapter 6] evaluates the Wiener filter-based method,
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page vii 12.9.2007 3:20pm Compositor Name: JGanesanMahalanobis distance, and subspace projection methods of change detection, with thechange detection performance illustrated by receiver operating characteristics (ROC)curves. In recent years, ICA and related approaches have presented many new potentialsin remote sensing information processing. A challenging task underlying many hyper-spectral imagery applications is decomposing a mixed pixel into a collection of reflec-tance spectra, called endmember signatures, and the corresponding abundance fractions.Chapter 19 [now Chapter 7] presents a new method for unsupervised endmemberextraction called vertex component analysis (VCA). The VCA algorithms presentedhave better or comparable performance as compared to two other techniques but requireless computational complexity. Other useful ICA applications in remote sensing includefeature extraction, and speckle reduction of SAR images. Chapter 20 [now Chapter 8]presents two different methods of SAR image speckle reduction using ICA, both makinguse of the FastICA algorithm. In two-dimensional time series modeling, Chapter 21 [nowChapter 9] makes use of a fractionally integrated autoregressive moving average(FARIMA) analysis to model the mean radial power spectral density of the sea SARimagery. Long-range dependence models are used in addition to the fractional sea surfacemodels for the simulation of the sea SAR image spectra at different sea states, with andwithout oil slicks at low computational cost. Returning to the image classification problem, Chapter 22 [now Chapter 10] deals withthe topics of pixel classification using Bayes classifier, region segmentation guided bymorphology and split-and-merge algorithm, region feature extraction, and region classi-fication. Chapter 23 [now Chapter 11] provides a tutorial presentation of different issues of datafusion for remote sensing applications. Data fusion can improve classification and for thedecision level fusion strategies, four multisensor classifiers are presented. Beyond thecurrently popular transform techniques, Chapter 24 [now Chapter 12] demonstrates thatHermite transform can be very useful for noise reduction and image fusion in remotesensing. The Hermite transform is an image representation model that mimics some of theimportant properties of human visual perception, namely local orientation analysis andthe Gaussian derivative model of early vision. Chapter 25 [now Chapter 13] is anotherchapter that demonstrates the importance of image fusion to improving sea ice classifi-cation performance, using backpropagation trained neural network and linear discrimin-ation analysis and texture features. Chapter 26 [now Chapter 14] is on the issue ofaccuracy assessment for which the Bradley–Terry model is adopted. Chapter 27 [nowChapter 15] is on land map classification using support vector machine, which has beenincreasingly popular as an effective classifier. The land map classification classifies thesurface of the Earth into categories such as water area, forests, factories or cities. Finally,with lossless data compression in mind, Chapter 28 [now Chapter 16] focuses on infor-mation-theoretic measure of the quality of multi-band remotely sensed digital images.The procedure relies on the estimation of parameters of the noise model. Results on imagesequences acquired by AVIRIS and ASTER imaging sensors offer an estimation of theinformation contents of each spectral band. With rapid technological advances in both sensor and processing technologies, a bookof this nature can only capture certain amount of current progress and results. However,if past experience offers any indication, the numerous mathematical techniques presentedwill give this volume a long lasting value. The sister volumes of this book are the other two books edited by myself. One isInformation Processing for Remote Sensing and the other is Frontiers of Remote SensingInformation Processing, both published by World Scientific in 1999 and 2003, respectively.I am grateful to all contributors of this volume for their important contribution and,
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page viii 12.9.2007 3:20pm Compositor Name: JGanesanin particular, to Dr. J.S. Lee, S. Serpico, L. Bruzzone and S. Omatu for chapter contribu-tions to all three volumes. Readers are advised to go over all three volumes for a morecomplete information on signal and image processing for remote sensing. C. H. Chen
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page ix 12.9.2007 3:20pm Compositor Name: JGanesanEditorChi Hau Chen was born on December 22nd, 1937. He received his Ph.D. in electricalengineering from Purdue University in 1965, M.S.E.E. degree from the University ofTennessee, Knoxville, in 1962, and B.S.E.E. degree from the National Taiwan Universityin 1959. He is currently chancellor professor of electrical and computer engineering at theUniversity of Massachusetts, Dartmouth, where he has taught since 1968. His researchareas are in statistical pattern recognition and signal/image processing with applicationsto remote sensing, geophysical, underwater acoustics, and nondestructive testing prob-lems, as well as computer vision for video surveillance, time series analysis, and neuralnetworks. Dr. Chen has published 25 books in his area of research. He is the editor of DigitalWaveform Processing and Recognition (CRC Press, 1982) and Signal Processing Handbook(Marcel Dekker, 1988). He is the chief editor of Handbook of Pattern Recognition andComputer Vision, volumes 1, 2, and 3 (World Scientific Publishing, 1993, 1999, and 2005,respectively). He is the editor of Fuzzy Logic and Neural Network Handbook (McGraw-Hill,1966). In the area of remote sensing, he is the editor of Information Processing for RemoteSensing and Frontiers of Remote Sensing Information Processing (World Scientific Publishing,1999 and 2003, respectively). He served as the associate editor of the IEEE Transactions on Acoustics Speech and SignalProcessing for 4 years, IEEE Transactions on Geoscience and Remote Sensing for 15 years, andsince 1986 he has been the associate editor of the International Journal of Pattern Recognitionand Artificial Intelligence. Dr. Chen has been a fellow of the Institutue of Electrical and Electronic Engineers(IEEE) since 1988, a life fellow of the IEEE since 2003, and a fellow of the InternationalAssociation of Pattern Recognition (IAPR) since 1996.
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page x 12.9.2007 3:20pm Compositor Name: JGanesan
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page xi 12.9.2007 3:20pm Compositor Name: JGanesanContributorsBruno Aiazzi Institute of Applied Physics, National Research Council, Florence, ItalySelim Aksoy Bilkent University, Ankara, TurkeyV.Yu. Alexandrov Nansen International Environmental and Remote Sensing Center, St. Petersburg, RussiaLuciano Alparone Department of Electronics and Telecommunications, University of Florence, Florence, ItalyStefano Baronti Institute of Applied Physics, National Research Council, Florence, ItalyJon Atli Benediktsson Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, IcelandFabrizio Berizzi Department of Information Engineering, University of Pisa, Pisa, ItalyMassimo Bertacca ISL-ALTRAN, Analysis and Simulation Group—Radar Systems Analysis and Signal Processing, Pisa, ItalyL.P. Bobylev Nansen International Environmental and Remote Sensing Center, St. Petersburg, RussiaA.V. Bogdanov Institute for Neuroinformatich, Bochum, GermanyFrancesca Bovolo Department of Information and Communication Technology, University of Trento, Trento, ItalyLorenzo Bruzzone Department of Information and Communication Technology, University of Trento, Trento, ItalyChi Hau Chen Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, North Dartmouth, MassachusettsSalim Chitroub Signal and Image Processing Laboratory, Department of Telecom- munication, Algiers, Algeria ´Jose M.B. Dias Department of Electrical and Computer Engineering, Instituto ´ Superior Tecnico, Av. Rovisco Pais, Lisbon, PortugalShinto Eguchi Institute of Statistical Mathematics, Tokyo, Japan
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page xii 12.9.2007 3:20pm Compositor Name: JGanesan ´Boris Escalante-Ramırez School of Engineering, National Autonomous University of Mexico, Mexico City, MexicoToru Fujinaka Osaka Prefecture University, Osaka, JapanGerrit Gort Department of Biometris, Wageningen University, The NetherlandsSveinn R. Joelsson Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, IcelandO.M. Johannessen Nansen Environmental and Remote Sensing Center, Bergen, NorwayDayalan Kasilingam Department of Electrical and Computer Engineering, Univer- sity of Massachusetts Dartmouth, North Dartmouth, MassachusettsHeesung Kwon U.S. Army Research Laboratory, Adelphi, MarylandJong-Sen Lee Remote Sensing Division, Naval Research Laboratory, Washington, D.C. ´Alejandra A. Lopez-Caloca Center for Geography and Geomatics Research, Mexico City, MexicoArko Lucieer Centre for Spatial Information Science (CenSIS), University of Tas- mania, AustraliaEnzo Dalle Mese Department of Information Engineering, University of Pisa, Pisa, ItalyGabriele Moser Department of Biophysical and Electronic Engineering, University of Genoa, Genoa, Italy ´Jose M.P. Nascimento Instituto Superior, de Eugenharia de Lisbon, Lisbon, PortugalNasser Nasrabadi U.S. Army Research Laboratory, Adelphi, MarylandRyuei Nishii Faculty of Mathematics, Kyusyu University, Fukuoka, JapanSigeru Omatu Osaka Prefecture University, Osaka, JapanS. Sandven Nansen Environmental and Remote Sensing Center, Bergen, NorwayDale L. Schuler Remote Sensing Division, Naval Research Laboratory, Washington, D.C.Massimo Selva Institute of Applied Physics, National Research Council, Florence, Italy
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page xiii 12.9.2007 3:20pm Compositor Name: JGanesanSebastiano B. Serpico Department of Biophysical and Electronic Engineering, University of Genoa, Genoa, ItalyAnne H.S. Solberg Department of Informatics, University of Oslo and Norwegian Computing Center, Oslo, NorwayAlfred Stein International Institute for Geo-Information Science and Earth Obser- vation, Enschede, The NetherlandsJohannes R. Sveinsson Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, IcelandMaria Tates U.S. Army Research Laboratory, Adelphi, Maryland, and Morgan State University, Baltimore, MarylandXianju Wang Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, North Dartmouth, MassachusettsCarl White Morgan State University, Baltimore, MarylandMichifumi Yoshioka Osaka Prefecture University, Osaka, Japan
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C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page xv 12.9.2007 3:20pm Compositor Name: JGanesanContents 1. Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface...............1 Dale L. Schuler, Jong-Sen Lee, and Dayalan Kasilingam 2. MRF-Based Remote-Sensing Image Classification with Automatic Model Parameter Estimation........................................................................39 Sebastiano B. Serpico and Gabriele Moser 3. Random Forest Classification of Remote Sensing Data ............................................61 Sveinn R. Joelsson, Jon Atli Benediktsson, and Johannes R. Sveinsson 4. Supervised Image Classification of Multi-Spectral Images Based on Statistical Machine Learning..........................................................................79 Ryuei Nishii and Shinto Eguchi 5. Unsupervised Change Detection in Multi-Temporal SAR Images .......................107 Lorenzo Bruzzone and Francesca Bovolo 6. Change-Detection Methods for Location of Mines in SAR Imagery....................135 Maria Tates, Nasser Nasrabadi, Heesung Kwon, and Carl White 7. Vertex Component Analysis: A Geometric-Based Approach to Unmix Hyperspectral Data .....................................................................149 ´ ´ Jose M.B. Dias and Jose M.P. Nascimento 8. Two ICA Approaches for SAR Image Enhancement................................................175 Chi Hau Chen, Xianju Wang, and Salim Chitroub 9. Long-Range Dependence Models for the Analysis and Discrimination of Sea-Surface Anomalies in Sea SAR Imagery ...........................189 Massimo Bertacca, Fabrizio Berizzi, and Enzo Dalle Mese10. Spatial Techniques for Image Classification..............................................................225 Selim Aksoy11. Data Fusion for Remote-Sensing Applications..........................................................249 Anne H.S. Solberg12. The Hermite Transform: An Efficient Tool for Noise Reduction and Image Fusion in Remote-Sensing .........................................................................273 ´ ´ Boris Escalante-Ramırez and Alejandra A. Lopez-Caloca13. Multi-Sensor Approach to Automated Classification of Sea Ice Image Data.....293 A.V. Bogdanov, S. Sandven, O.M. Johannessen, V.Yu. Alexandrov, and L.P. Bobylev
C.H. Chen/Image Processing for Remote Sensing 66641_C000 Final Proof page xvi 12.9.2007 3:20pm Compositor Name: JGanesan14. Use of the Bradley–Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image...................325 Alfred Stein, Gerrit Gort, and Arko Lucieer15. SAR Image Classification by Support Vector Machine...........................................341 Michifumi Yoshioka, Toru Fujinaka, and Sigeru Omatu16. Quality Assessment of Remote-Sensing Multi-Band Optical Images ..................355 Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Massimo SelvaIndex .............................................................................................................................................377
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 1 3.9.2007 2:00pm Compositor Name: JGanesan1Polarimetric SAR Techniques for RemoteSensing of the Ocean SurfaceDale L. Schuler, Jong-Sen Lee, and Dayalan KasilingamCONTENTS1.1 Introduction ........................................................................................................................... 21.2 Measurement of Directional Slopes and Wave Spectra ................................................. 2 1.2.1 Single Polarization versus Fully Polarimetric SAR Techniques ....................... 2 1.2.2 Single-Polarization SAR Measurements of Ocean Surface Properties............. 3 1.2.3 Measurement of Ocean Wave Slopes Using Polarimetric SAR Data .............. 5 188.8.131.52 Orientation Angle Measurement of Azimuth Slopes.......................... 5 184.108.40.206 Orientation Angle Measurement Using the Circular-Pol Algorithm.................................................................................................... 5 1.2.4 Ocean Wave Spectra Measured Using Orientation Angles............................... 6 1.2.5 Two-Scale Ocean-Scattering Model: Effect on the Orientation Angle Measurement ................................................................................................. 9 1.2.6 Alpha Parameter Measurement of Range Slopes.............................................. 11 220.127.116.11 Cloude–Pottier Decomposition Theorem and the Alpha Parameter .................................................................................................. 11 18.104.22.168 Alpha Parameter Sensitivity to Range Traveling Waves.................. 13 22.214.171.124 Alpha Parameter Measurement of Range Slopes and Wave Spectra............................................................................................ 14 1.2.7 Measured Wave Properties and Comparisons with Buoy Data..................... 16 126.96.36.199 Coastal Wave Measurements: Gualala River Study Site .................. 16 188.8.131.52 Open-Ocean Measurements: San Francisco Study Site..................... 181.3 Polarimetric Measurement of Ocean Wave–Current Interactions.............................. 20 1.3.1 Introduction ............................................................................................................. 20 1.3.2 Orientation Angle Changes Caused by Wave–Current Interactions ............. 21 1.3.3 Orientation Angle Changes at Ocean Current Fronts ...................................... 25 1.3.4 Modeling SAR Images of Wave–Current Interactions ..................................... 251.4 Ocean Surface Feature Mapping Using Current-Driven Slick Patterns .................... 27 1.4.1 Introduction ............................................................................................................. 27 1.4.2 Classification Algorithm........................................................................................ 31 184.108.40.206 Unsupervised Classification of Ocean Surface Features .................. 31 220.127.116.11 Classification Using Alpha–Entropy Values and the Wishart Classifier .................................................................................... 31 18.104.22.168 Comparative Mapping of Slicks Using Other Classification Algorithms................................................................................................ 341.5 Conclusions.......................................................................................................................... 34References ..................................................................................................................................... 36 1
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 2 3.9.2007 2:00pm Compositor Name: JGanesan2 Image Processing for Remote Sensing1.1 IntroductionSelected methods that use synthetic aperture radar (SAR) image data to remotely senseocean surfaces are described in this chapter. Fully polarimetric SAR radars provide muchmore usable information than conventional single-polarization radars. Algorithms, pre-sented here, to measure directional wave spectra, wave slopes, wave–current interactions,and current-driven surface features use this additional information. Polarimetric techniques that measure directional wave slopes and spectra with datacollected from a single aircraft, or satellite, collection pass are described here. Conven-tional single-polarization backscatter cross-section measurements require two orthogonalpasses and a complex SAR modulation transfer function (MTF) to determine vector slopesand directional wave spectra. The algorithm to measure wave spectra is described in Section 1.2. In the azimuth(flight) direction, wave-induced perturbations of the polarimetric orientation angle areused to sense the azimuth component of the wave slopes. In the orthogonal rangedirection, a technique involving an alpha parameter from the well-known Cloude–Pottierentropy/anisotropy/averaged alpha (H/A/) polarimetric decomposition theorem is used to measure the range slope component. Both measurement types are highly sensitiveto ocean wave slopes and are directional. Together, they form a means of using polari-metric SAR image data to make complete directional measurements of ocean wave slopesand wave slope spectra. NASA Jet Propulsion Laboratory airborne SAR (AIRSAR) P-, L-, and C-band dataobtained during flights over the coastal areas of California are used as wave-fieldexamples. Wave parameters measured using the polarimetric methods are compared withthose obtained using in situ NOAA National Data Buoy Center (NDBC) buoy products. In a second topic (Section 1.3), polarization orientation angles are used to remotelysense ocean wave slope distribution changes caused by ocean wave–current interactions.The wave–current features studied include surface manifestations of ocean internalwaves and wave interactions with current fronts. A model , developed at the Naval Research Laboratory (NRL), is used to determinethe parametric dependencies of the orientation angle on internal wave current, wind-wave direction, and wind-wave speed. An empirical relation is cited to relate orientationangle perturbations to the underlying parametric dependencies . A third topic (Section 1.4) deals with the detection and classification of biogenic slickfields. Various techniques, using the Cloude–Pottier decomposition and Wishart clas-sifier, are used to classify the slicks. An application utilizing current-driven oceanfeatures, marked by slick patterns, is used to map spiral eddies. Finally, a relatedtechnique, using the polarimetric orientation angle, is used to segment slick fields fromocean wave slopes.1.2 Measurement of Directional Slopes and Wave Spectra1.2.1 Single Polarization versus Fully Polarimetric SAR TechniquesSAR systems conventionally use backscatter intensity-based algorithms  to measurephysical ocean wave parameters. SAR instruments, operating at a single-polarization,measure wave-induced backscatter cross section, or sigma-0, modulations that can be
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 3 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 3developed into estimates of surface wave slopes or wave spectra. These measurements,however, require a parametrically complex MTF to relate the SAR backscatter meas-urements to the physical ocean wave properties . Section 1.2.3 through Section 1.2.6 outline a means of using fully polarimetric SAR(POLSAR) data with algorithms  to measure ocean wave slopes. In the Fourier-trans-form domain, this orthogonal slope information is used to estimate a complete directionalocean wave slope spectrum. A parametrically simple measurement of the slope is madeby using POLSAR-based algorithms. Modulations of the polarization orientation angle, u, are largely caused by wavestraveling in the azimuth direction. The modulations are, to a lesser extent, also affectedby range traveling waves. A method, originally used in topographic measurements ,has been applied to the ocean and used to measure wave slopes. The method measuresvector components of ocean wave slopes and wave spectra. Slopes smaller than 18 aremeasurable for ocean surfaces using this method. An eigenvector or eigenvalue decomposition average parameter , described in Ref. , is used to measure wave slopes in the orthogonal range direction. Waves in therange direction cause modulation of the local incidence angle f, which, in turn, changesthe value of . The alpha parameter is ‘‘roll-invariant.’’ This means that it is not affected by slopes in the azimuth direction. Likewise, for ocean wave measurements, the orien-tation angle u parameter is largely insensitive to slopes in the range direction. Analgorithm employing both (, u) is, therefore, capable of measuring slopes in any direction. The ability to measure a physical parameter in two orthogonal directionswithin an individual resolution cell is rare. Microwave instruments, generally, musthave a two-dimensional (2D) imaging or scanning capability to obtain information intwo orthogonal directions. Motion-induced nonlinear ‘‘velocity-bunching’’ effects still present difficulties for wavemeasurements in the azimuth direction using POLSAR data. These difficulties are dealtwith by using the same proven algorithms [3,7] that reduce nonlinearities for single-polarization SAR measurements.1.2.2 Single-Polarization SAR Measurements of Ocean Surface PropertiesSAR systems have previously been used for imaging ocean features such as surfacewaves, shallow-water bathymetry, internal waves, current boundaries, slicks, and shipwakes . In all of these applications, the modulation of the SAR image intensity by theocean feature makes the feature visible in the image . When imaging ocean surfacewaves, the main modulation mechanisms have been identified as tilt modulation, hydro-dynamic modulation, and velocity bunching . Tilt modulation is due to changes in thelocal incidence angle caused by the surface wave slopes . Tilt modulation is strongestfor waves traveling in the range direction. Hydrodynamic modulation is due to thehydrodynamic interactions between the long-scale surface waves and the short-scalesurface (Bragg) waves that contribute most of the backscatter at moderate incidenceangles . Velocity bunching is a modulation process that is unique to SAR imagingsystems . It is a result of the azimuth shifting of scatterers in the image plane, owingto the motion of the scattering surface. Velocity bunching is the highest for azimuthtraveling waves. In the past, considerable effort had gone into retrieving quantitative surface waveinformation from SAR images of ocean surface waves . Data from satellite SARmissions, such as ERS 1 and 2 and RADARSAT 1 and 2, had been used to estimatesurface wave spectra from SAR image information. Generally, wave height and wave
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 4 3.9.2007 2:00pm Compositor Name: JGanesan4 Image Processing for Remote Sensingslope spectra are used as quantitative overall descriptors of the ocean surface waveproperties . Over the years, several different techniques have been developed forretrieving wave spectra from SAR image spectra [7,15,16]. Linear techniques, such asthose having a linear MTF, are used to relate the wave spectrum to the imagespectrum. Individual MTFs are derived for the three primary modulation mechanisms.A transformation based on the MTF is used to retrieve the wave spectrum from theSAR image spectrum. Since the technique is linear, it does not account for any non-linear processes in the modulation mechanisms. It has been shown that SAR imagemodulation is nonlinear under certain ocean surface conditions. As the sea stateincreases, the degree of nonlinear behavior generally increases. Under these conditions,the linear methods do not provide accurate quantitative estimates of the wave spectra. Thus, the linear transfer function method has limited utility and can be used as aqualitative indicator. More accurate estimates of wave spectra require the use of non-linear inversion techniques . Several nonlinear inversion techniques have been developed for retrieving wavespectra from SAR image spectra. Most of these techniques are based on a techniquedeveloped in Ref. . The original method used an iterative technique to estimate thewave spectrum from the image spectrum. Initial estimates are obtained using a lineartransfer function similar to the one used in Ref. . These estimates are used as inputsin the forward SAR imaging model, and the revised image spectrum is used toiteratively correct the previous estimate of the wave spectra. The accuracy of thistechnique is dependent on the specific SAR imaging model. Improvements to thistechnique  have incorporated closed-form descriptions of the nonlinear transferfunction, which relates the wave spectrum to the SAR image spectrum. However,this transfer function also has to be evaluated iteratively. Further improvements tothis method have been suggested in Refs. [3,18]. In this method, a cross-spectrum isgenerated between different looks of the same ocean wave scene. The primary advan-tage of this method is that it resolves the 1808 ambiguity [3,18] of the wave direction.This method also reduces the effects of speckle in the SAR spectrum. Methods thatincorporate additional a posteriori information about the wave field, which improvesthe accuracy of these nonlinear methods, have also been developed in recent years . In all of the slope-retrieval methods, the one nonlinear mechanism that may completelydestroy wave structure is velocity bunching [3,7]. Velocity bunching is a result of movingscatterers on the ocean surface either bunching or dilating in the SAR image domain. Theshifting of the scatterers in the azimuth direction may, in extreme conditions, result in thedestruction of the wave structure in the SAR image. SAR imaging simulations were performed at different range-to-velocity (R/V) ratios tostudy the effect of velocity bunching on the slope-retrieval algorithms. When the (R/V)ratio is artificially increased to large values, the effects of velocity bunching are expectedto destroy the wave structure in the slope estimates. Simulations of the imaging processfor a wide range of radar-viewing conditions indicate that the slope structure is preservedin the presence of moderate velocity-bunching modulation. It can be argued that forvelocity bunching to affect the slope estimates, the (R/V) ratio has to be significantlylarger than 100 s. The two data sets discussed here are designated ‘‘Gualala River’’ and‘‘San Francisco.’’ The Gualala river data set has the longest waves and it also produces thebest results. The R/V ratio for the AIRSAR missions was 59 s (Gualala) and 55 s (SanFrancisco). These values suggest that the effects of velocity bunching are present, but arenot sufficiently strong to significantly affect the slope-retrieval process. However, forspaceborne SAR imaging applications, where the (R / V) ratio may be greater than 100 s,the effects of velocity bunching may limit the utility of all methods, especially in highsea states.
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 5 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 51.2.3 Measurement of Ocean Wave Slopes Using Polarimetric SAR DataIn this section, the techniques that were developed for the measurement of ocean surfaceslopes and wave spectra using the capabilities of fully polarimetric radars are discussed.Wave-induced perturbations of the polarization orientation angle are used to directlymeasure slopes for azimuth traveling waves. This technique is accurate for scatteringfrom surface resolution cells where the sea return can be represented as a two-scaleBragg-scattering process.22.214.171.124 Orientation Angle Measurement of Azimuth SlopesIt has been shown  that by measuring the orientation angle shift in the polarizationsignature, one can determine the effects of the azimuth surface tilts. In particular, the shift inthe orientation angle is related to the azimuth surface tilt, the local incidence angle, and, to alesser degree, the range tilt. This relationship is derived  and independently verified  as tan v tan u ¼ (1:1) sin f À tan g cos fwhere u, tan v, tan g, and f are the shifts in the orientation angle, the azimuth slope, theground range slope, and the radar look angle, respectively. According to Equation 1.1, theazimuth tilts may be estimated from the shift in the orientation angle, if the look angle andrange tilt are known. The orthogonal range slope tan g can be estimated using the value of the local incidenceangle associated with the alpha parameter for each pixel. The azimuth slope tan v and therange slope tan g provide complete slope information for each image pixel. For the ocean surface at scales of the size of the AIRSAR resolution cell (6.6 m Â 8.2 m),the averaged tilt angles are small and the denominator in Equation 1.1 may be approxi-mated by sin f for a wide range of look angles, cos f, and ground range slope, tan g,values. Under this approximation, the ocean azimuth slope, tan v, is written as tan v ﬃ ( sin f) Á tan u (1:2)The above equation is important because it provides a direct link between polarimetric SARmeasurable parameters and physical slopes on the ocean surface. This estimation of ocean slopesrelies only on (1) the knowledge of the radar look angle (generally known from the SAR viewinggeometry) and (2) the measurement of the wave-perturbed orientation angle. In ocean areaswhere the average scattering mechanism is predominantly tilted-Bragg scatter, the orientationangle can be measured accurately for angular changes 18, as demonstrated in Ref. . POLSAR data can be represented by the scattering matrix for single-look complex dataand by the Stokes matrix, the covariance matrix, or the coherency matrix for multi-lookdata. An orientation angle shift causes rotation of all these matrices about the line of sight.Several methods have been developed to estimate the azimuth slope–induced orientationangles for terrain and ocean applications. The ‘‘polarization signature maximum’’ methodand the ‘‘circular polarization’’ method have proven to be the two most effective methods.Complete details of these methods and the relation of the orientation angle to orthogonalslopes and radar parameters are given [21,22].126.96.36.199 Orientation Angle Measurement Using the Circular-Pol AlgorithmImage processing was done with both the polarization signature maximum and thecircular polarization algorithms. The results indicate that for ocean images a sign-ificant improvement in wave visibility is achieved when a circular polarization algor-ithm is chosen. In addition to this improvement, the circular polarization algorithm is
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 6 3.9.2007 2:00pm Compositor Name: JGanesan6 Image Processing for Remote Sensingcomputationally more efficient. Therefore, the circular polarization algorithm methodwas chosen to estimate orientation angles. The most sensitive circular polarization esti-mator , which involves RR (right-hand transmit, right-hand receive) and LL (left-handtransmit, left-hand receive) terms, is * u ¼ [Arg(hSRR SLL i) þ p]=4 (1:3)A linear-pol basis has a similar transmit-and-receive convention, but the terms (HH, VV,HV, VH) involve horizontal (H) and vertical (V) transmitted, or received, components.The known relations between a circular-pol basis and a linear-pol basis are SRR ¼ (SHH À SVV þ i2SHV )=2 (1:4) SLL ¼ (SVV À SHH þ i2SHV )=2Using the above equation, the Arg term of Equation 1.3 can be written as 0 1 * À4Re h(SHH À SVV )SHV i u ¼ Arg(hSRR S* i) ¼ tanÀ1 @ A (1:5) LL ÀhjSHH À SVV j2 i þ 4hjSHV j2 iThe above equation gives the orientation angle, u, in terms of three of the terms of thelinear-pol coherency matrix. This algorithm has been proven to be successful in Ref. .An example of the accuracy is cited from related earlier studies involving wave–currentinteractions . In these studies, it has been shown that small wave slope asymmetriescould be accurately detected as changes in the orientation angle. These small asymmetrieshad been predicted by theory  and their detection indicates the sensitivity of thecircular-pol orientation angle measurement.1.2.4 Ocean Wave Spectra Measured Using Orientation AnglesNASA/JPL/AIRSAR data were taken (1994) at L-band imaging a northern Californiacoastal area near the town of Gualala (Mendocino County) and the Gualala River. Thisdata set was used to determine if the azimuth component of an ocean wave spectrumcould be measured using orientation angle modulation. The radar resolution cell haddimensions of 6.6 m (range direction) and 8.2 m (azimuth direction), and 3 Â 3 boxcaraveraging was done to the data inputted into the orientation angle algorithms. Figure 1.1 is an L-band, VV-pol, pseudo color-coded image of a northern Californiacoastal area and the selected measurement study site. A wave system with an estimateddominant wavelength of 157 m is propagating through the site with a wind-wavedirection of 3068 (estimates from wave spectra, Figure 1.4). The scattering geometry fora single average tilt radar resolution cell is shown in Figure 1.2. Modulations in thepolarization orientation angle induced by azimuth traveling ocean waves in the studyarea are shown in Figure 1.3a and a histogram of the orientation angles is given in Figure1.3b. An orientation angle spectrum versus wave number for azimuth direction wavespropagating in the study area is given in Figure 1.4. The white rings correspond to oceanwavelengths of 50, 100, 150, and 200 m. The dominant 157-m wave is propagating at aheading of 3068. Figure 1.5a and Figure 1.5b give plots of spectral intensity versus wavenumber (a) for wave-induced orientation angle modulations and (b) for single-polariza-tion (VV-pol)-intensity modulations. The plots are of wave spectra taken in the directionthat maximizes the dominant wave peak. The orientation angle–dominant wave peak
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 7 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 7 N Northern California Pacific Ocean Wave direction Gualala (306؇) River Range Study−site box (512 ϫ 512) Flight direction (azimuth)FIGURE 1.1 (See color insert following page 240.)An L-band, VV-pol, AIRSAR image, of northern California coastal waters (Gualala River dataset), showing oceanwaves propagating through a study-site box.(Figure 1.5a) has a significantly higher signal and background ratio than the conventionalintensity-based VV-pol-dominant wave peak (Figure 1.5b). Finally, the orientation angles measured within the study sites were converted intoazimuth direction slopes using an average incidence angle and Equation 1.2. From theestimates of these values, the ocean rms azimuth slopes were computed. These values aregiven in Table 1.1.
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 8 3.9.2007 2:00pm Compositor Name: JGanesan8 Image Processing for Remote Sensing z Ra V dar H Incidence plane N (Surface normal) I1 f (Ground range) y Tilted ocean resolution cell h) ut zim (A x , I2FIGURE 1.2Geometry of scattering from a single, tilted, resolution cell. In the Gualala River dataset, the resolution cell hasdimensions 6.6 m (range direction) and 8.2 m (azimuth direction). Study area: orientation angles (a)FIGURE 1.3(a) Image of modulations in the orientation angle, u, in the study site and (b) a histogram of the distribution ofstudy-site u values. (continued)
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 9 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 9 (b) 1.2 x 104 1.0 x 104 Occurence number 8.0 x 103 6.0 x 103 4.0 x 103 2.0 x 103 0 –6 –4 –2 0 2 4 6 Orientation angle (deg)FIGURE 1.3 (continued)1.2.5 Two-Scale Ocean-Scattering Model: Effect on the Orientation Angle MeasurementIn Section 188.8.131.52, Equation 1.5 is given for the orientation angle. This equation gives theorientation angle u as a function of three terms from the polarimetric coherency matrix T.Scattering has only been considered as occurring from a slightly rough, tilted surfaceequal to or greater than the size of the radar resolution cell (see Figure 1.2). The surface isplanar and has a single tilt us. This section examines the effects of having a distribution ofazimuth tilts, p (w), within the resolution cell, rather than a single averaged tilt. For single-look or multi-look processed data, the coherency matrix is defined as 2 D E 3 jSHH þ SVV j2 h(SHH þ SVV )(SHH À SVV )*i 2 (SHH þ SVV )S*HV 16 6 D E 7 7T ¼ hkk*T i ¼ 6 h(SHH À SVV )(SHH þ SVV )*i jSHH À SVV j2 2 (SHH À SVV )S* 7 24 D E HV 5 2hSHV (SHH þ SVV )*i 2hSHV (SHH À SVV )*i 4 jSHV j2 (1:6) 2 3 SHH þ SVV 1with k ¼ pﬃﬃﬃ4 SHH À SVV 5 2 2S HV We now follow the approach of Cloude and Pottier . The composite surface consists offlat, slightly rough ‘‘facets,’’ which are tilted in the azimuth direction with a distribution of tilts, p(w): 1 jw À sj b p(w) ¼ 2b (1:7) 0 otherwisewhere s is the average surface azimuth tilt of the entire radar resolution cell. The effect on T of having both (1) a mean bias azimuthal tilt us and (2) a distributionof azimuthal tilts b has been calculated in Ref.  as 2 3 A B sin c(2b) cos 2us ÀB sin c(2b) sin 2usT ¼ 4 B* sin c(2b) cos 2us 2C( sin 2us þ sin c(4b) cos 4us ) 2 C(1 À 2 sin c(4b)) sin 4us 5 ÀB* sin c(2b) sin 2us C(1 À 2 sin c(4b)) sin 4us 2C( cos2 2us À sin c(4b) cos 4us ) (1:8)
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 10 3.9.2007 2:00pm Compositor Name: JGanesan10 Image Processing for Remote Sensing 50 m Dominant wave: 157 m 100 m 200 m 150 m Wave direction 306؇FIGURE 1.4 (See color insert following page 240.)Orientation angle spectra versus wave number for azimuth direction waves propagating through the study site.The white rings correspond to 50, 100, 150, and 200 m. The dominant wave, of wavelength 157 m, is propagatingat a heading of 3068.where sin c(x) is ¼ sin(x)/x and A ¼ jSHH þ SVV j2 , B ¼ (SHH þ SVV )(S* À S* ), C ¼ 0:5jSHH À SVV j2 HH VVEquation 1.8 reveals the changes due to the tilt distribution (b) and bias (us) that occur inall terms, except in the term A ¼ jSHH þ SVVj2, which is roll-invariant. In the correspond-ing expression for the orientation angle, all of the other terms, except the denominatorterm hjSHH À SVVj2i, are modified À Á ! À4Re h(SHH À SVV )S* iHV u ¼ Arg(hSRR S* i) ¼ tanÀ1 (1:9) ÀhjSHH À SVV j2 i þ 4hjSHV j2 i LLFrom Equation 1.8 and Equation 1.9, it can be determined that the exact estimation of theorientation angle u becomes more difficult as the distribution of ocean tilts b becomesstronger and wider because the ocean surface becomes progressively rougher.
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 11 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 11 (a) L-band orientation angle wave spectra 0.50 0.40 Spectral intensity 0.30 0.20 0.10 0.00 0.02 0.04 0.06 0.08 Azimuthal wave number (b) L-band, VV-intensity wave spectra 0.50 0.40 Spectral intensity 0.30 0.20 0.10 0.00 0.00 0.02 0.04 0.06 0.08 0.10 Azimuthal wave numberFIGURE 1.5Plots of spectral intensity versus wave number (a) for wave-induced orientation angle modulationsand (b) for VV-pol intensity modulations. The plots are taken in the propagation direction of the dominantwave (3068).1.2.6 Alpha Parameter Measurement of Range SlopesA second measurement technique is needed to remotely sense waves that have significantpropagation direction components in the range direction. The technique must be moresensitive than current intensity-based techniques that depend on tilt and hydrodynamicmodulations. Physically based POLSAR measurements of ocean slopes in the rangedirection are achieved using a technique involving the ‘‘alpha’’ parameter of theCloude–Pottier polarimetric decomposition theorem .184.108.40.206 Cloude–Pottier Decomposition Theorem and the Alpha ParameterThe Cloude–Pottier entropy, anisotropy, and the alpha polarization decomposition the-orem  introduce a new parameterization of the eigenvectors of the 3 Â 3 averagedcoherency matrix hjTji in the form
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 12 3.9.2007 2:00pm Compositor Name: JGanesan12 Image Processing for Remote SensingTABLE 1.1Northern California: Gualala Coastal Results In Situ Measurement Instrument Bodega Bay, CA 3-m Point Arena, CA Orientation Angle Alpha AngleParameter Discus Buoy 46013 Wind Station Method MethodDominant wave 10.0 N/A 10.03 From dominant 10.2 From dominant period (s) wave number wave numberDominant 156 From period, N/A 157 From wave 162 From wave wavelength (m) depth spectra spectraDominant wave 320 Est. from 284 Est. from 306 From wave 306 From wave direction (8) wind direction wind direction spectra spectrarms slopes azimuth N/A N/A 1.58 N/A direction (8)rms slopes range N/A N/A N/A 1.36 direction (8)Estimate of wave 2.4 Significant N/A 2.16 Est. from rms 1.92 Est. from rms height (m) wave height slope, wave number slope, wave numberDate: 7/15/94; data start time (UTC): 20:04:44 (BB, PA), 20:02:98 (AIRSAR); wind speed: 1.0 m/s (BB), 2.9 m/s(PA) Mean ¼ 1.95 m/s; wind direction: 3208 (BB), 2848 (PA), mean ¼ 3028; Buoy: ‘‘Bodega Bay’’ (46013) ¼ BB;location: 38.23 N 123.33 W; water depth: 122.5 m; wind station: ‘‘Point Arena’’ (PTAC – 1) ¼ PA; location: 38.968N, 123.74 W; study-site location: 38839.60 N, 123835.80 W. 2 3 l1 0 0 hjTji ¼ [U3 ] Á4 0 l2 0 5Á [U3 ]*T (1:10) 0 0 l3where 2 3 cos a1 cos a2 ejf2 cos a3 ejf3 jf 4 [U3 ] ¼ e sin a1 cos b1 ejd1 sin a2 cos b2 ejd2 sin a3 cos b3 ejd3 5 (1:11) sin a1 sin b1 ejg1 sin a2 sin b2 ejg2 sin a3 sin b3 ejg3The average estimate of the alpha parameter is a ¼ P1 a 1 þ P2 a 2 þ P3 a 3 (1:12)where li Pi ¼ Pj¼3 : (1:13) j¼1 ljThe individual alphas are for the three eigenvectors and the Ps are the probabilitiesdefined with respect to the eigenvalues. In this method, the average alpha is used and is,for simplicity, defined as a. For the ocean backscatter, the contributions to the average alpha are dominated, however, by the first eigenvalue or eigenvector term. The alpha parameter, developed from the Cloude–Pottier polarimetric scatteringdecomposition theorem , has desirable directional measurement properties. It is(1) roll-invariant in the azimuth direction and (2) in the range direction, it is highlysensitive to wave-induced modulations of f in the local incidence angle f. Thus, the
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 13 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 13alpha parameter is well suited for measuring wave components traveling in therange direction, and discriminates against wave components traveling in the azimuthdirection.220.127.116.11 Alpha Parameter Sensitivity to Range Traveling WavesThe alpha angle sensitivity to range traveling waves may be estimated using the smallperturbation scattering model (SPSM) as a basis. For flat, slightly rough scattering areasthat can be characterized by Bragg scattering, the scattering matrix has the form ! SHH 0 S¼ (1:14) 0 SVVBragg-scattering coefficients SVV and SHH are given by qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ «r À sin2 fi cos fi À («r À 1)( sin2 fi À «r (1 þ sin2 fi ) ) SHH ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ and SVV ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ2 cos fi þ «r À sin2 fi «r cos fi þ «r À sin2 fi (1:15)The alpha angle is defined such that the eigenvectors of the coherency matrix T areparameterized by a vector k as 2 3 cos a k ¼ 4 sin a cos bejd 5 sin a sin bejg (1:16)For Bragg scattering, one may assume that there is only one dominant eigenvector(depolarization is negligible) and the eigenvector is given by 2 3 SVV þ SHH k ¼ 4 SVV À SHH 5 0 (1:17)Since there is only one dominant eigenvector, for Bragg scattering, a ¼ a1. For ahorizontal, slightly rough resolution cell, the orientation angle b ¼ 0, and d may be setto zero. With these constraints, comparing Equation 1.16 and Equation 1.17 yields SVV À SHH tan a ¼ (1:18) SVV þ SHHFor « ! 1, SVV ¼ 1 þ sin2 fi and SHH ¼ cos2 fi (1:19)which yields tan a ¼ sin2 fi (1:20)Figure 1.6 shows the alpha angle as a function of incidence angle for « ! 1 (blue) andfor (red) « ¼ 80–70j, which is a representative dielectric constant of sea water. Thesensitivity to alpha values to incidence angle changes (this is effectively the polarimetric
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 14 3.9.2007 2:00pm Compositor Name: JGanesan14 Image Processing for Remote Sensing 45 40 35 30 Alpha angle (deg) 25 20 15 10 5 0 0 10 20 30 40 50 60 70 80 90 Incidence angle (deg)FIGURE 1.6 (See color insert following page 240.)Small perturbation model dependence of alpha on the incidence angle. The red curve is for a dielectric constantrepresentative of sea water (80–70j) and the blue curve is for a perfectly conducting surface.MTF) as the range slope estimation is dependent on the derivative of a with respect to fi.For « ! 1 Da sin 2fi ¼ (1:21) Dfi 1 þ sin4 fiFigure 1.7 shows this curve (blue) and the exact curve for « ¼ 80–70j (red). Note that forthe typical AIRSAR range of incidence angles (20–608), across the swath, the effective MTFis high (0.5).18.104.22.168 Alpha Parameter Measurement of Range Slopes and Wave SpectraModel studies  result in an estimate of what the parametric relation a versus theincidence angle f should be for an assumed Bragg-scatter model. The sensitivity (i.e.,the slope of the curve of a(f)) was large enough (Figure 1.6) to warrant investigationusing real POLSAR ocean backscatter data. In Figure 1.8a, a curve of a versus the incidence angle f is given for a strip of Gualaladata in the range direction that has been averaged 10 pixels in the azimuth direction. Thiscurve shows a high sensitivity for the slope of a(f). Figure 1.8b gives a histogram of thefrequency of occurrence of the alpha values. The curve of Figure 1.8a was smoothed by utilizing a least-square fit of the a(f) data toa third-order polynomial function. This closely fitting curve was used to transform the avalues into corresponding incidence angle f perturbations. Pottier  used a model-basedapproach and fitted a third-order polynomial to the a(f) (red curve) of Figure 1.6 insteadof using the smoothed, actual, image a(f) data. A distribution of f values has been madeand the rms range slope value has been determined. The rms range slope values for thedata sets are given in Table 1.1 and Table 1.2.
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 15 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 15 0.9 0.8 0.7Derivative of alpha (f ) 0.6 0.5 0.4 0.3 0.2 0.1 FIGURE 1.7 (See color insert following page 240.) Derivative of alpha with respect 0 to the incidence angle. The red curve is for a 0 10 20 30 40 50 60 70 80 90 sea water dielectric and the blue curve is for Incidence angle (F ) (deg) a perfectly conducting surface. Finally, to measure an alpha wave spectrum, an image of the study area is formed with themean of a(f) removed line by line in the range direction. An FFT of the study area results inthe wave spectrum that is shown in Figure 1.9. The spectrum of Figure 1.9 is an alphaspectrum in the range direction. It can be converted to a range direction wave slopespectrum by transforming the slope values obtained from the smoothed alpha, a(f), values. (a) 40 Alpha (deg) 20 0 20 30 40 50 60 Incidence angle (deg)FIGURE 1.8Empirical determination of the (a) sensitivity of the alpha parameter to the radar incidence angle (for GualalaRiver data) and (b) a histogram of the alpha values occurring within the study site. (continued)
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 16 3.9.2007 2:00pm Compositor Name: JGanesan16 Image Processing for Remote Sensing (b) 1.4 × 104 1.2 × 104 Alpha value occurrences 1.0 × 104 8.0 × 103 6.0 × 103 4.0 × 103 2.0 × 103 0 –6 –4 –2 0 2 4 6 Alpha parameter perturbation (deg)FIGURE 1.8 (continued)(b) a histogram of the alpha values occurring within the study site.1.2.7 Measured Wave Properties and Comparisons with Buoy DataThe ocean wave properties estimated from the L- and P-band SAR data sets and thealgorithms are the (1) dominant wavelength, (2) dominant wave direction, (3) rms slopes(azimuth and range), and (4) average dominant wave height. The NOAA NDBC buoysprovided data on the (1) dominant wave period, (2) wind speed and direction, (3)significant wave height, and (4) wave classification (swell and wind waves). Both the Gualala and the San Francisco data sets involved waves classified as swell.Estimates of the average wave period can be determined either from buoy data or fromthe SAR-determined dominant wave number and water depth (see Equation 1.22). The dominant wavelength and direction are obtained from the wave spectra (see Figure1.4 and Figure 1.9). The rms slopes in the azimuth direction are determined from thedistribution of orientation angles converted to slope angles using Equation 1.2. The rmsslopes in the range direction are determined by the distribution of alpha angles convertedto slope angles using values of the smoothed curve fitted to the data of Figure 1.8a. Finally, an estimate of the average wave height, Hd, of the dominant wave was madeusing the peak-to-trough rms slope in the propagation direction Srms and the dominantwavelength ld. The estimated average dominant wave height was then determined fromtan (Srms) ¼ Hd/(ld/2). This average dominant wave height estimate was compared withthe (related) significant wave height provided by the NDBC buoy. The results of themeasurement comparisons are given in Table 1.1 and Table 22.214.171.124.7.1 Coastal Wave Measurements: Gualala River Study SiteFor the Gualala River data set, parameters were calculated to characterize ocean wavespresent in the study area. Table 1.1 gives a summary of the ocean parameters thatwere determined using the data set as well as wind conditions and air and sea temperaturesat the nearby NDBC buoy (‘‘Bodega Bay’’) and wind station (‘‘Point Arena’’) sites. The mostimportant measured SAR parameters were rms wave slopes (azimuth and range direc-tions), rms wave height, dominant wave period, and dominant wavelength. These quan-tities were estimated using the full-polarization data and the NDBC buoy data.
TABLE 1.2Open Ocean: Pacific Swell Results In Situ Measurement Instrument San Francisco, CA, 3 m Half Moon Bay, CA, 3 m Orientation Alpha AngleParameter Discus Buoy 46026 Discus Buoy 46012 Angle Method MethodDominant wave period (s) 15.7 15.7 15.17 From dominant 15.23 From dominant wave number wave numberDominant wavelength (m) 376 From period, depth 364 From period, depth 359 From wave spectra 362 From wave spectraDominant wave direction (8) 289 Est. from wind 280 Est. from wind 265 From wave spectra 265 From wave spectra direction directionrms slopes azimuth direction (8) N/A N/A 0.92 N/Arms slopes range direction (8) N/A N/A N/A 0.86Estimate of wave height (m) 3.10 Significant 2.80 Significant 2.88 Est. from rms slope, 2.72 Est. from rms slope, C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 17 wave height wave height wave number wave numberDate: 7/17/88; data start time (UTC): 00:45:26 (Buoys SF, HMB), 00:52:28 (AIRSAR); wind speed: 8.1 m/s (SF), 5.0 m/s (HMB), mean ¼ 6.55 m/s; wind direction: 2898 (SF),2808 (HMB), mean ¼ 284.58; Buoys: ‘‘San Francisco’’ (46026) ¼ SF; location: 37.75 N 122.82 W; water depth: 52.1 m; ‘‘Half Moon Bay’’ (46012) ¼ HMB; location: 37.36 N 122.88 Polarimetric SAR Techniques for Remote Sensing of the Ocean SurfaceW; water depth: 87.8 m. 3.9.2007 2:00pm Compositor Name: JGanesan 17
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 18 3.9.2007 2:00pm Compositor Name: JGanesan18 Image Processing for Remote Sensing Wave specturm Dominant 50 m wave: 162 m 200 m 100 m 150 m Wave direction 306؇FIGURE 1.9 (See color insert following page 240.)Spectrum of waves in the range direction using the alpha parameter from the Cloude–Pottier decompositionmethod. Wave direction is 3068 and dominant wavelength is 162 m. The dominant wave at the Bodega Bay buoy during the measurement period is classi-fied as a long wavelength swell. The contribution from wind wave systems or other swellcomponents is small relative to the single dominant wave system. Using the surfacegravity wave dispersion relation, one can calculate the dominant wavelength at thisbuoy location where the water depth is 122.5 m. The dispersion relation for surfacewater waves at finite depth is v2 ¼ gkW tan h(kH) W (1:22)where vW is the wave frequency, kW is the wave number (2p/l), and H is the water depth.The calculated value for l is given in Table 1.1. A spectral profile similar to Figure 1.5a was developed for the alpha parameter tech-nique and a dominant wave was measured having a wavelength of 156 m and a propa-gation direction of 3068. Estimates of the ocean parameters obtained using the orientationangle and alpha angle algorithms are summarized in Table 126.96.36.199.7.2 Open-Ocean Measurements: San Francisco Study SiteAIRSAR P-band image data were obtained for an ocean swell traveling in the azimuthdirection. The location of this image was to the west of San Francisco Bay. It is a valuable
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 19 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 19 N Wave directionFIGURE 1.10P-band span image of near-azimuth traveling (2658) swell in the Pacific Ocean off the coast of California nearSan Francisco.data set because its location is near two NDBC buoys (‘‘San Francisco’’ and ‘‘Half-MoonBay’’). Figure 1.10 gives a SPAN image of the ocean scene. The long-wavelength swell isclearly visible. The covariance matrix data was first Lee-filtered to reduce speckle noise and was then corrected radiometrically. A polarimetric signature was developed for a 512 Â 512 segment of the image andsome distortion was noted. Measuring the distribution of the phase between the HH-poland VV-pol backscatter returns eliminated this distortion. For the ocean, this distribu-tion should have a mean nearly equal to zero. The recalibration procedure set the meanto zero and the distortion in the polarimetric signature was corrected. Figure 1.11a givesa plot of the spectral intensity (cross-section modulation) versus the wave number in thedirection of the dominant wave propagation. Figure 1.11b presents a spectrum oforientation angles versus the wave number. The major peak, caused by the visibleswell, in both plots occurs at a wave number of 0.0175 mÀ1 or a wavelength of 359 m.Using Equation 1.22, the dominant wavelength was calculated at the San Francisco/HalfMoon Bay buoy positions and depths. Estimates of the wave parameters developedfrom this data set using the orientation and alpha angle algorithms are presented inTable 1.2.
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 20 3.9.2007 2:00pm Compositor Name: JGanesan20 Image Processing for Remote Sensing (a) 0.20 0.15 Spectral intensity 0.10 0.05 0.00 0.00 0.02 0.04 0.06 0.08 Azimuthal wave number (b) 0.20 0.15 Spectral intensity 0.10 0.05 0.00 0.00 0.02 0.04 0.06 0.08 Azimuthal wave numberFIGURE 1.11(a) Wave number spectrum of P-band intensity modulations and (b) a wave number spectrum of orientationangle modulations. The plots are taken in the propagation direction of the dominant wave (2658).1.3 Polarimetric Measurement of Ocean Wave–Current Interactions1.3.1 IntroductionStudies have been carried out on the use of polarization orientation angles to remotelysense ocean wave slope distribution changes caused by wave–current interactions. Thewave–current features studied here involve the surface manifestations of internal waves[1,25,26–29,30–32] and wave modifications at oceanic current fronts. Studies have shown that polarimetric SAR data may be used to measure bare surfaceroughness  and terrain topography [34,35]. Techniques have also been developed formeasuring directional ocean wave spectra .
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 21 3.9.2007 2:00pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 21 The polarimetric SAR image data used in all of the studies are NASA JPL/AIRSAR, P-,L-, and C-band, quad-pol microwave backscatter data. AIRSAR images of internal waveswere obtained from the 1992 Joint US/Russia Internal Wave Remote Sensing Experiment(JUSREX’92) conducted in the New York Bight [25,26]. AIRSAR data on current fronts were obtained during the NRL Gulf Stream Experiment(NRL-GS’90). The NRL experiment is described in Ref. . Extensive sea-truth is avail-able for both of these experiments. These studies were motivated by the observation thatstrong perturbations occur in the polarization orientation angle u in the vicinity of internalwaves and current fronts. The remote sensing of orientation angle changes associatedwith internal waves and current fronts are applications that have only recently beeninvestigated [27–29]. Orientation angle changes should also occur for the related SARapplication involving surface expressions of shallow-water bathymetry . In the studies outlined here, polarization orientation angle changes are shown to beassociated with wave–current interaction features. Orientation angle changes are not,however, produced by all types of ocean surface features. For example, orientationangle changes have been successfully used here to discriminate internal wave signaturesfrom other ocean features, such as surfactant slicks, which produce no mean orientationangle changes.1.3.2 Orientation Angle Changes Caused by Wave–Current InteractionsA study was undertaken to determine the effect that several important types of wave–current interactions have on the polarization orientation angle. The study involved bothactual SAR data and an NRL theoretical model described in Ref. . An example of a JPL/AIRSAR VV-polarization, L-band image of several strong, inter-secting, internal wave packets is given in Figure 1.12. Packets of internal waves aregenerated from parent solitons as the soliton propagates into shallower water at, in thiscase, the continental shelf break. The white arrow in Figure 1.12 indicates the propagationdirection of a wedge of internal waves (bounded by the dashed lines). The packetmembers within the area of this wedge were investigated. Radar cross-section (s0) intensity perturbations for the type of internal waves encoun-tered in the New York Bight have been calculated in [30,31,39] and others. Relatedperturbations also occur in the ocean wave height and slope spectra. For the solitonsoften found in the New York Bight area, these perturbations become significantly largerfor ocean wavelengths longer than about 0.25 m and shorter than 10–20 m. Thus, thestudy is essentially concerned with slope changes to meter-length wave scales. The AIR-SAR slant range resolution cell size for these data is 6.6 m, and the azimuth resolution cellsize is 12.1 m. These resolutions are fine enough for the SAR backscatter to be affected byperturbed wave slopes (meter-length scales). The changes in the orientation angle caused by these wave perturbations are seen inFigure 1.13. The magnitude of these perturbations covers a range u ¼ [À1 to þ1]. Theorientation angle perturbations have a large spatial extent (100 m for the internal wavesoliton width). The hypothesis assumed was that wave–current interactions make the meter wave-length slope distributions asymmetric. A profile of orientation angle perturbationscaused by the internal wave study packet is given in Figure 1.14a. The values areobtained along the propagation vector line of Figure 1.12. Figure 1.14b gives a compari-son of the orientation angle profile (solid line) and a normalized VV-pol backscatterintensity profile (dotted-dash line) along the same interval. Note that the orientationangle positive peaks (white stripe areas, Figure 1.13) align with the negative troughs
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 22 3.9.2007 2:00pm Compositor Name: JGanesan22 Image Processing for Remote Sensing Azimuth direction Range direction α Internal waves packet propagation vectorFIGURE 1.12AIRSAR L-band, VV-pol image of internal wave intersecting packets in the New York Bight. The arrow indicatesthe propagation direction for the chosen study packet (within the dashed lines). The angle a relates to the SAR/packet coordinates. The image intensity has been normalized by the overall average.(black areas, Figure 1.12). In the direction orthogonal to the propagation vector, everypoint is averaged 5 Â 5 pixels along the profile. The ratio of the maximum of u caused bythe soliton to the average values of u within the ambient ocean is quite large. Thecurrent-induced asymmetry creates a mean wave slope that is manifested as a meanorientation angle. The relation between the tangent of the orientation angle u, wave slopes in the radarazimuth and ground range directions (tan v, tan g), and the radar look angle f from is given by Equation 1.1, and for a given look angle f the average orientation angletangent is
C.H. Chen/Image Processing for Remote Sensing 66641_C001 Final Proof page 23 3.9.2007 2:01pm Compositor Name: JGanesanPolarimetric SAR Techniques for Remote Sensing of the Ocean Surface 23 –1.0Њ 0Њ +1.0Њ Orientation angleFIGURE 1.13The orientation angle image of the internal wave packets in the New York Bight. The area within the wedge(dashed lines) was studied intensively. ðð p p htan ui ¼ tan u(v,g) Á P(v,g) dg dv (1:23) 0 0where P(v,g) is the joint probability distribution function for the surface slopes in theazimuth and range directions. If the slopes are zero-meaned, but P(v,g) is skewed, thenthe mean orientation angle may not be zero even though the mean azimuth and rangeslopes are zero. It is evident from the above equation that both the azimuth and the range