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E-raamat: Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 20-Oct-2016
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316028599
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Hyperspectral Imaging Remote Sensing: Physics, Sensors, and AlgorithmsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 20-Oct-2016
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316028599
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"A practical and self-contained guide to the principles, techniques, models and tools of imaging spectroscopy. Bringing together material from essential physics and digital signal processing, it covers key topics such as sensor design and calibration, atmospheric inversion and model techniques, and processing and exploitation algorithms. Readers will learn how to apply the main algorithms to practical problems, how to choose the best algorithm for a particular application, and how to process and interpret hyperspectral imaging data. A wealth of additional materials accompany the book online, including example projects and data for students, and problem solutions and viewgraphs for instructors. This is an essential text for senior undergraduate and graduate students looking to learn the fundamentals of imaging spectroscopy, and an invaluable reference for scientists and engineers working in the field"--

Arvustused

'The authors have done a masterful job of integrating and presenting the diverse subjects that form the foundation of the field of hyperspectral imaging and applications. This comprehensive textbook will clearly become one of the standard references for all who wish to learn about both fundamentals and advanced applications in this important field.' Charles Bachmann, Rochester Institute of Technology, New York 'An extraordinarily comprehensive treatment of hyperspectral remote sensing by three of the field's noted authorities. An indispensable reference for those new to the field and for the seasoned professional.' Ronald G. Resmini, George Mason University, Virginia 'The authors have offered a comprehensive and up-to-date treatment of hyperspectral imaging modalities. A wide readership, including scientists and graduate students involved with spectral imaging modalities, could benefit from this book.' Axel Mainzer Koenig, Optics and Photonics

Muu info

Understand the seminal principles, current techniques, and tools of imaging spectroscopy with this self-contained introductory guide.
Preface xi
1 Introduction
1(35)
1.1 Introduction
1(2)
1.2 Infrared Sensing Phenomenology
3(5)
1.3 Hyperspectral Imaging Sensors
8(7)
1.4 Data Preprocessing
15(4)
1.5 Data Exploitation Algorithms
19(6)
1.6 Applications of Imaging Spectroscopy
25(3)
1.7 History of Spectral Remote Sensing
28(5)
1.8 Summary and Further Reading
33(1)
1.9 Book Organization
33(3)
2 The Remote Sensing Environment
36(81)
2.1 Electromagnetic Radiation
36(5)
2.2 Diffraction and Interference
41(9)
2.3 Basic Radiometry
50(5)
2.4 Radiation Sources
55(6)
2.5 Quantum Mechanical Results
61(15)
2.6 Spectral Line Shapes
76(8)
2.7 Atmospheric Scattering Essentials
84(11)
2.8 Optical Thickness
95(7)
2.9 Properties of the Atmosphere
102(12)
2.10 Summary and Further Reading
114(3)
3 Spectral Properties of Materials
117(37)
3.1 Introduction
117(1)
3.2 Geometrical Description
118(8)
3.3 Directional Emissivity
126(2)
3.4 Volume Scattering of Materials
128(3)
3.5 Elements of Mineral Spectroscopy
131(13)
3.6 Organic Spectroscopy
144(4)
3.7 Man-Made Materials
148(2)
3.8 Long Wave Infrared Spectra
150(1)
3.9 Summary and Further Reading
151(3)
4 Imaging Spectrometers
154(74)
4.1 Telescopes
155(6)
4.2 Imaging Spectrometer Common Concepts
161(10)
4.3 Dispersive Imaging Spectrometer Fundamentals
171(24)
4.4 Dispersive Imaging Spectrometer Designs
195(14)
4.5 Interference Imaging Spectrometer Fundamentals
209(13)
4.6 Data Acquisition with Imaging Spectrometers
222(2)
4.7 Summary and Further Reading
224(4)
5 Imaging Spectrometer Characterization and Data Calibration
228(67)
5.1 Introduction
228(1)
5.2 Application of the Measurement Equation
229(2)
5.3 Spectral Characterization
231(7)
5.4 Radiometric Characterization
238(23)
5.5 Spatial Characterization
261(1)
5.6 Advanced Calibration Techniques
262(1)
5.7 Error Analysis
263(6)
5.8 Radiometric Performance Modeling
269(11)
5.9 Vicarious Calibration
280(12)
5.10 Summary and Further Reading
292(3)
6 Radiative Transfer and Atmospheric Compensation
295(65)
6.1 Radiative Transfer
295(8)
6.2 General Solution to the Radiative Transfer Equation
303(9)
6.3 Modeling Tools of Radiative Transfer
312(10)
6.4 Reflective Atmospheric Compensation
322(8)
6.5 Estimating Model Parameters from Scene Data
330(14)
6.6 Reflective Compensation Implementation
344(7)
6.7 Atmospheric Compensation in the Thermal Infrared
351(7)
6.8 Summary and Further Reading
358(2)
7 Statistical Models for Spectral Data
360(46)
7.1 Univariate Distributions -- Variance
360(3)
7.2 Bivariate Distributions -- Covariance
363(4)
7.3 Random Vectors -- Covariance Matrix
367(4)
7.4 Multivariate Distributions
371(12)
7.5 Maximum Likelihood Parameter Estimation
383(4)
7.6 Statistical Analysis of Hyperspectral Imaging Data
387(7)
7.7 Gaussian Mixture Models
394(9)
7.8 Summary and Further Reading
403(3)
8 Linear Spectral Transformations
406(37)
8.1 Introduction
406(2)
8.2 Implications of High-Dimensionality
408(3)
8.3 Principal Components Analysis: Theory
411(10)
8.4 Principal Components Analysis: Application
421(3)
8.5 Diagonalizing Two Different Covariance Matrices
424(4)
8.6 Maximum Noise Fraction (MNF) Transform
428(1)
8.7 Canonical Correlation Analysis (CCA)
429(3)
8.8 Linear Discriminant Analysis
432(5)
8.9 Linear Spectral-Band Estimation
437(4)
8.10 Summary and Further Reading
441(2)
9 Spectral Mixture Analysis
443(51)
9.1 Spectral Mixing
443(3)
9.2 The Linear Mixing Model
446(5)
9.3 Endmember Determination Techniques
451(2)
9.4 Fill-Fraction Estimation Techniques
453(1)
9.5 The Method of Least Squares Estimation
454(9)
9.6 Least Squares Computations
463(3)
9.7 Statistical Properties of Least Squares Estimators
466(2)
9.8 Generalized Least Squares Estimation
468(2)
9.9 Maximum Likelihood Estimation
470(1)
9.10 Regularized Least Squares Problems
471(4)
9.11 Consequences of Model Misspecification
475(2)
9.12 Hypotheses Tests for Model Parameters
477(3)
9.13 Model Selection Criteria
480(2)
9.14 Variable Selection in Linear Signal Models
482(5)
9.15 Linear Spectral Mixture Analysis in Practice
487(5)
9.16 Summary and Further Reading
492(2)
10 Signal Detection Theory
494(57)
10.1 A Simple Decision-Making Problem
494(2)
10.2 Elements of Statistical Hypotheses Testing
496(7)
10.3 The General Gaussian Detection Problem
503(8)
10.4 Gaussian Detectors in the Presence of Unknowns
511(6)
10.5 Matched Filter and Maximization of Deflection
517(5)
10.6 Performance Analysis of Matched Filter Detectors
522(11)
10.7 Detectors for Signals in Subspace Clutter and Isotropic Noise
533(6)
10.8 Eigenvector Matched Filters
539(2)
10.9 Robust Matched Filters
541(6)
10.10 Adaptive Matched Filter Detectors
547(1)
10.11 Summary and Further Reading
548(3)
11 Hyperspectral Data Exploitation
551(70)
11.1 Target Detection in the Reflective Infrared
551(19)
11.2 Target Detection Performance Assessment
570(7)
11.3 False Alarm Mitigation and Target Identification
577(4)
11.4 Spectral Landscape Classification
581(5)
11.5 Change Detection
586(5)
11.6 Unique Aspects of Spectral Exploitation in the Thermal Infrared
591(4)
11.7 Remote Sensing of Chemical Clouds: Physics
595(10)
11.8 Remote Sensing of Chemical Clouds: Algorithms
605(15)
11.9 Summary and Further Reading
620(1)
Appendix Introduction to Gaussian Optics
621(33)
A.1 The Optical Path
621(3)
A.2 Ideal Image Formation
624(4)
A.3 The Paraxial Approximation
628(5)
A.4 The Limiting Aperture
633(7)
A.5 Example: The Cooke Triplet
640(2)
A.6 Afocal Systems
642(2)
A.7 Aberration Theory
644(8)
A.8 Summary and Further Reading
652(2)
Bibliography 654(24)
Index 678
Dimitris G. Manolakis is a senior member of technical staff at the Lincoln Laboratory, Massachusetts Institute of Technology. He is the co-author of Applied Digital Signal Processing (Cambridge, 2011), and has taught at various institutions including Northeastern University, Boston, Boston College, Massachusetts, and Worcester Polytechnic Institute, Massachusetts. He is an IEEE Fellow, and in 2013 he received the IEEE Signal Processing Society Education Award. Ronald B. Lockwood is a member of technical staff at the Lincoln Laboratory, Massachusetts Institute of Technology. He previously worked at the Air Force Research Laboratory where he developed imaging spectrometers for both space-based and air-borne applications. He has also developed vicarious calibration techniques in collaboration with colleagues at the University of Arizona and the NASA Goddard Space Flight Center. Thomas W. Cooley is the Senior Scientist for Space Situational Awareness at the US Air Force Research Laboratory, and has made significant contributions to the fields of atmospheric compensation and spectral data analysis. He developed the ARTEMIS sensor program, which was successfully launched in 2009, and has published over 70 research papers.
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