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E-raamat: Remote Sensing Image Fusion

(Nello Carrara Institute of Applied Physics, Florence, Italy), (Nello Carrara Institute of Applied Physics, Florence, Italy), (University of Florence, Italy),
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Remote Sensing Image FusionSuurem pilt
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A synthesis of more than ten years of experience, Remote Sensing Image Fusion covers methods specifically designed for remote sensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and panchromatic images, and fusion of data from heterogeneous sensors such as optical and synthetic aperture radar (SAR) images and integration of thermal and visible/near-infrared images. They also explore new trends of signal/image processing, such as compressive sensing and sparse signal representations.

The book brings a new perspective to a multidisciplinary research field that is becoming increasingly articulate and comprehensive. It fosters signal/image processing methodologies toward the goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple processing methods tailored to the specific features of the images may be winning in terms of reliable performance over more complex algorithms based on mathematical theories and models unconstrained from the physical behaviors of the instruments.

Ultimately, the book covers the births and developments of three generations of RS image fusion. Established textbooks are mainly concerned with the earliest generation of methods. This book focuses on second generation methods you can use now and new trends that may become third generation methods. Only the lessons learned with second generation methods will be capable of fostering the excellence among the myriad of methods that are proposed almost every day by the scientific literature.

Arvustused

" a very impressive piece of work. should become the bedside book for anyone interested in image fusion for remote sensing, from the graduate student, the PhD candidate or the researcher to the engineer or the enduser addressing any application based on remote sensing data and in need for an improved resolution." Jocelyn Chanussot, Grenoble Institute of Technology

" the first comprehensive book to be published on this important topic. The authors are leaders in the field and have been on top of this important subject for the last two decades. The book is highly recommended." Prof. Jón Atli Benediktsson, Faculty of Electrical and Computer Engineering, University of Iceland, IEEE Fellow, SPIE Fellow, 2011-2012 President IEEE Geoscience and Remote Sensing Society, 2003-2008 Editor-in-Chief, IEEE Trans. on Geoscience and Remote Sensing

List of Figures xi
List of Tables xv
Foreword xvii
Preface xix
Symbol Description xxi
List of Acronyms xxiii
1 Instructions for Use 1(10)
1.1 Introduction
1(1)
1.2 Aim and Scope of the Book
2(1)
1.3 Organization of Contents
3(5)
1.4 Concluding Remarks
8(3)
2 Sensors and Image Data Products 11(40)
2.1 Introduction
12(1)
2.2 Basic Concepts
12(12)
2.2.1 Definition of Terms
14(1)
2.2.2 Spatial Resolution
15(6)
2.2.2.1 Point spread function
17(1)
2.2.2.2 Modulation transfer function
18(3)
2.2.2.3 Classification of sensors on spatial resolution
21(1)
2.2.3 Radiometric Resolution
21(1)
2.2.4 Spectral Resolution
22(1)
2.2.5 Temporal Resolution
23(1)
2.3 Acquisition Strategy
24(4)
2.3.1 Whisk Broom Sensors
25(1)
2.3.2 Push Broom Sensors
26(2)
2.4 Optical Sensors
28(13)
2.4.1 Reflected Radiance Sensors
31(1)
2.4.1.1 Reflection
31(1)
2.4.2 HR and VHR Sensors
32(8)
2.4.2.1 IKONOS
33(2)
2.4.2.2 QuickBird
35(1)
2.4.2.3 WorldView
36(2)
2.4.2.4 GeoEye
38(1)
2.4.2.5 Pleiades
39(1)
2.4.2.6 FormoSat-2 and KoMPSat
39(1)
2.4.3 Thermal Imaging Sensors
40(1)
2.5 Active Sensors
41(9)
2.5.1 Radar and Synthetic Aperture Radar
41(6)
2.5.1.1 Across-track spatial resolution
42(2)
2.5.1.2 Along-track spatial resolution
44(1)
2.5.1.3 Synthetic aperture radar (SAR)
44(1)
2.5.1.4 SAR images and speckle
45(2)
2.5.2 SAR Sensors
47(2)
2.5.3 LiDAR
49(1)
2.6 Concluding Remarks
50(1)
3 Quality Assessment of Fusion 51(16)
3.1 Introduction
51(1)
3.2 Quality Definition for Pansharpening
52(10)
3.2.1 Statistical Quality/Distortion Indices
54(3)
3.2.1.1 Indices for scalar valued images
54(2)
3.2.1.2 Indices for vector valued images
56(1)
3.2.2 Protocols Established for Pansharpening
57(4)
3.2.2.1 Wald's protocol
58(1)
3.2.2.2 Zhou's protocol
59(1)
3.2.2.3 QNR protocol
60(1)
3.2.2.4 Khan's protocol
61(1)
3.2.3 Extension to Hyperspectral Pansharpening
61(1)
3.2.4 Extension to Thermal V-NIR Sharpening
62(1)
3.3 Assessment of Optical and SAR Fusion
62(2)
3.4 Concluding Remarks
64(3)
4 Image Registration and Interpolation 67(34)
4.1 Introduction
67(2)
4.2 Image Registration
69(9)
4.2.1 Definitions
69(1)
4.2.2 Geometric Corrections
70(3)
4.2.2.1 Sources of geometric distortions
70(3)
4.2.3 Point Mapping Methods: Image Transformations
73(4)
4.2.3.1 2-D polynomials
73(3)
4.2.3.2 3-D polynomials
76(1)
4.2.4 Resampling
77(1)
4.2.5 Other Registration Techniques
78(1)
4.3 Image Interpolation
78(21)
4.3.1 Problem Statement
79(1)
4.3.2 Theoretical Fundamentals of Digital Interpolation
79(2)
4.3.2.1 Interpolation by continuous-time kernels
79(1)
4.3.2.2 Interpolation by digital kernels
80(1)
4.3.3 Ideal and Practical Interpolators
81(2)
4.3.4 Piecewise Local Polynomial Kernels
83(1)
4.3.5 Derivation of Piecewise Local Polynomial Kernels
84(5)
4.3.5.1 Continuous-time local polynomial kernels
84(1)
4.3.5.2 Odd and even local polynomial kernels
85(4)
4.3.6 Interpolation of MS Data for Pansharpening
89(3)
4.3.6.1 One-dimensional case
89(1)
4.3.6.2 Two-dimensional case
90(2)
4.3.7 Interpolation Assessment for Pansharpening
92(10)
4.3.7.1 Evaluation criteria
92(1)
4.3.7.2 Dataset
92(1)
4.3.7.3 Results and discussion
93(6)
4.4 Concluding Remarks
99(2)
5 Multiresolution Analysis for Image Fusion 101(22)
5.1 Introduction
101(1)
5.2 Multiresolution Analysis
102(3)
5.2.1 Orthogonal Wavelets
104(1)
5.2.2 Biorthogonal Wavelets
105(1)
5.3 Multilevel Unbalanced Tree Structures
105(3)
5.3.1 Critically Decimated Schemes
106(1)
5.3.2 Translation Invariant Schemes
106(2)
5.4 2-D Multiresolution Analysis
108(4)
5.4.1 2-D Undecimated Separable Analysis
109(2)
5.4.2 A-Trous Analysis
111(1)
5.5 Gaussian and Laplacian Pyramids
112(5)
5.5.1 Generalized Laplacian Pyramid
115(2)
5.6 Nonseparable MRA
117(4)
5.6.1 Curvelets
118(1)
5.6.2 Contourlets
118(3)
5.7 Concluding Remarks
121(2)
6 Spectral Transforms for Multiband Image Fusion 123(20)
6.1 Introduction
123(1)
6.2 RGB-to-IHS Transform and Its Implementations
124(12)
6.2.1 Linear IHS Cylindric Transforms
125(3)
6.2.2 Nonlinear IHS Transforms
128(3)
6.2.2.1 Nonlinear IHS triangle transform
129(1)
6.2.2.2 Nonlinear HSV hexcone transform
130(1)
6.2.2.3 Nonlinear HSL bi-hexcone transform
131(1)
6.2.3 Generalization of IHS Transforms for MS Image Fusion
131(5)
6.2.3.1 Linear IHS
132(2)
6.2.3.2 Nonlinear IHS
134(2)
6.3 PCA Transform
136(3)
6.3.1 Decorrelation Properties of PCA
136(1)
6.3.2 PCA Transform for Image Fusion
137(2)
6.4 Gram-Schmidt Transform
139(3)
6.4.1 Gram-Schmidt Orthogonalization Procedure
139(1)
6.4.2 Gram-Schmidt Spectral Sharpening
140(2)
6.5 Concluding Remarks
142(1)
7 Pansharpening of Multispectral Images 143(34)
7.1 Introduction
143(2)
7.2 Classification of Pansharpening Methods
145(2)
7.3 A Critical Review of Pansharpening Methods
147(18)
7.3.1 Component Substitution
148(5)
7.3.1.1 Generalized Intensity-Hue-Saturation
151(1)
7.3.1.2 Principal Component Analysis
152(1)
7.3.1.3 Gram-Schmidt orthogonalization
152(1)
7.3.2 Optimization of CS Fusion Methods
153(2)
7.3.3 Multiresolution Analysis
155(3)
7.3.4 Optimization of MRA Based on Instrument MTF
158(6)
7.3.4.1 ATW
161(1)
7.3.4.2 GLP
161(2)
7.3.4.3 MRA-optimized CS
163(1)
7.3.5 Hybrid Methods
164(1)
7.4 Simulation Results and Discussion
165(10)
7.4.1 Fusion of IKONOS Data
165(7)
7.4.2 Fusion of QuickBird Data
172(2)
7.4.3 Discussion
174(1)
7.5 Concluding Remarks
175(2)
8 Pansharpening of Hyperspectral Images 177(16)
8.1 Introduction
177(2)
8.2 Multispectral to Hyperspectral Pansharpening
179(2)
8.3 Literature Review
181(3)
8.3.1 BDSD-MMSE Fusion
181(2)
8.3.2 Fusion with a Constrained Spectral Angle
183(1)
8.4 Simulation Results
184(7)
8.4.1 BDSD-MMSE Algorithm
186(1)
8.4.2 CSA Algorithm
186(2)
8.4.3 Discussion
188(3)
8.5 Concluding Remarks
191(2)
9 Effects of Aliasing and Misalignments on Pansharpening 193(30)
9.1 Introduction
193(2)
9.2 Mathematical Formulation
195(1)
9.2.1 CS-Based Methods
195(1)
9.2.2 MRA-Based Methods
195(1)
9.3 Sensitivity to Aliasing
196(7)
9.3.1 CS-Based Methods
196(1)
9.3.2 MRA-Based Methods
197(1)
9.3.2.1 ATW-based fusion
197(1)
9.3.2.2 GLP-based fusion
197(1)
9.3.3 Results and Discussion
198(5)
9.3.3.1 CS vs. MRA-ATW
199(1)
9.3.3.2 MRA-GLP vs. MRA-ATW
200(3)
9.4 Sensitivity to Spatial Misalignments
203(9)
9.4.1 MRA-Based Methods
205(1)
9.4.2 CS-Based Methods
205(1)
9.4.3 Results and Discussion
206(6)
9.4.3.1 Misregistration
206(4)
9.4.3.2 Interpolation shifts
210(2)
9.5 Sensitivity to Temporal Misalignments
212(9)
9.5.1 Results and Discussion
216(5)
9.6 Concluding Remarks
221(2)
10 Fusion of Images from Heterogeneous Sensors 223(24)
10.1 Introduction
223(1)
10.2 Fusion of Thermal and Optical Data
224(9)
10.2.1 Background and Literature Review
225(1)
10.2.2 Fusion of ASTER Data
226(7)
10.2.2.1 ASTER imaging sensor
227(1)
10.2.2.2 A Fusion scheme for TIR ASTER data
227(1)
10.2.2.3 Interchannel correlation
228(2)
10.2.2.4 Results and discussion
230(3)
10.3 Fusion of Optical and SAR Data
233(12)
10.3.1 Problem Statement
234(1)
10.3.2 Literature Review
235(1)
10.3.3 Quality Issues
236(1)
10.3.4 Fusion of Landsat 7 ETM+ and ERS SAR Data
237(13)
10.3.4.1 Fusion scheme
237(2)
10.3.4.2 Generalized intensity modulation
239(1)
10.3.4.3 Simulation results
240(4)
10.3.4.4 Summary and discussion
244(1)
10.4 Concluding Remarks
245(2)
11 New Trends of Remote Sensing Image Fusion 247(18)
11.1 Introduction
247(1)
11.2 Restoration-Based Approaches
248(2)
11.3 Sparse Representation
250(8)
11.3.1 Sparse Image Fusion for Spatial-Spectral Fusion
250(4)
11.3.2 Sparse Spatio-Temporal Image Fusion
254(4)
11.4 Bayesian Approaches
258(2)
11.5 Variational Approaches
260(2)
11.6 Methods Based on New Spectral Transformations
262(1)
11.7 Concluding Remarks
263(2)
12 Conclusions and Perspectives 265(10)
12.1 Introduction
265(1)
12.2 State of the Art of Pansharpening
266(2)
12.2.1 Interpolation
266(1)
12.2.2 Pansharpening Based on CS
267(1)
12.2.3 Pansharpening Based on MRA
267(1)
12.2.4 Hybrid Methods
268(1)
12.3 Multispectral to Hyperspectral Pansharpening
268(1)
12.4 Fusion of Heterogeneous Datasets
269(1)
12.5 New Paradigms of Fusion
270(1)
12.6 Concluding Remarks
271(4)
Bibliography 275(26)
Index 301
Luciano Alparone received the Laurea degree (with honors) in electronic engineering from the University of Florence, Florence, Italy, in 1985 and the Ph.D. degree from the Italian Ministry of Education in 1990. During the spring of 2000 and summer of 2001, he was a Visiting Researcher at the Tampere International Centre for Signal Processing, Tampere, Finland. Since 2002, he has been an Associate Professor with the Images and Communications Laboratory, Department of Electronics and Telecommunications, University of Florence, where he currently holds the courses of Telecommunication Systems and Remote Sensing for Environmental Monitoring. He participated in several research projects funded by the Italian Ministry of University (MIUR), the Italian Space Agency (ASI), the French Space Agency (CNES), and the European Space Agency (ESA). Recently, he has been the Principal Investigator of a project funded by ASI on the processing of Cosmo-SkyMed SAR data. His research interests are data compression for remote sensing applications, multiresolution image analysis and processing, multisensor data fusion, analysis, and processing of SAR images. He has authored or coauthored over 60 papers in peer-reviewed journals and a total of 300 publications. Dr. Alparone was a corecipient of the 2004 Geoscience and Remote Sensing Letters Prize Paper Award for the study on "A global quality measurement of pansharpened multispectral imagery."

Bruno Aiazzi received the Laurea degree in electronic engineering from the University of Florence, Florence, Italy, in 1991. Since 2001, he has been a Researcher with the Institute of Applied Physics "Nello Carrara" (IFAC-CNR), which is located in the CNR Area di Ricerca di Firenze, Florence. He is currently Senior Researcher in the IFAC-CNR Institute. He has been working in several international research projects funded by the main European space agencies (ASI, ESA, CNES) on remote sensing topics: image quality definition and measurement, with applications to advanced hyperspectral sensors, adaptive methods for lossless and near-lossless data compression in satellite scenarios, multispectral and hyperspectral pansharpening algorithms, automatic estimation of multitemporal changes, and theoretical definition of statistic-based features for classification purposes. He is the coauthor of more than 30 papers published in international peer-reviewed journals, and a total of almost 200 publications. He is the recipient of the IEEE Geoscience and Remote Sensing Society Certificate of Appreciation as the winner of the 2006 Data Fusion Contest, Fusion on Multispectral and Panchromatic Images. He is an IEEE member.

Stefano Baronti is Senior Researcher at the Institute of Applied Physics "Nello Carrara" (IFAC) of the National Research Council (CNR) of Italy. He was born in Florence, Italy, in 1954. He received the Laurea degree in Electronic Engineering from the University of Florence, in 1980 and joined CNR in 1985, as a Researcher of IFAC, where he is currently responsible for the research unit Systems, techniques, processing and analysis of multidimensional multiresolution remote sensing data. He has been involved in several projects funded by the Italian, French, and European Space Agencies. His research interests include computer vision applications, image compression, processing of optical and microwave remote sensing SAR images, and fusion and quality assessment of remote sensing data. He is the coauthor of more than 270 papers published in international peer-reviewed journals, proceedings of international conferences and book chapters. He is a member of the IEEE Geoscience and Remote Sensing Society (GRSS) and of the IEEE Signal Processing Society and participates in the GRSS Technical Committee on Data Fusion. He is the recipient of the IEEE GRSS 2004 Letter Prize Paper Award and the IEEE Geoscience and Remote Sensing Society Certificate of Appreciation as the winner of the 2006 Data Fusion Contest, Fusion on Multispectral and Panchromatic Images.

Andrea Garzelli is Associate Professor of Telecommunications at the Department of Information Engineering of the University of Siena where he currently teaches the undergraduate and postgraduate courses on "Digital Signal Processing". He obtained the "Laurea" degree (summa cum laude) in Electronic Engineering and the Ph.D. degree in Computer Science and Telecommunication Engineering from the University of Florence, Italy, in 1991 and 1995, respectively. From 1995 to 2001 he has been Assistant Professor at the Department of Information Engineering of the University of Siena. He has been Program Coordinator of both graduate and Master degrees in Telecommunication Engineering at the University of Siena from 2002 to 2009. His research interests are in signal and image analysis, processing, and communication: nonlinear filtering, analysis of SAR images, and image classification and fusion for optical and SAR remote sensing applications. He is author of more than 150 scientific publications, including peer-reviewed journals, book chapters and conference proceedings. Dr. Garzelli is a Member of the IEEE Geoscience and Remote Sensing Society - Data Fusion Committee. He is the 2004 recipient, with his co-authors, of the IEEE Geoscience and Remote Sensing Society Letters Prize Paper Award for the paper "A Global Quality Measurement of Pan-Sharpened Multispectral Imagery". On January 2006, he has been elected Senior Member of the IEEE. He has been the invited speaker at the "Image and Signal Processing for Remote Sensing" Conference at SPIE Remote Sensing 2011 giving a talk on "Image sharpening: solutions and implementation issues."
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