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Spie Press Book

Hyperspectral Remote Sensing
Author(s): Michael T. Eismann
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Book Description

Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment that captures its multidisciplinary nature.

The content is oriented toward the physical principles of hyperspectral remote sensing as opposed to applications of hyperspectral technology. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available in this technology area and apply their knowledge to the understanding of material spectral properties, the design of hyperspectral systems, the analysis of hyperspectral imagery, and the application of the technology to specific problems.

Hyperspectral Remote Sensing is the 2018 winner of the Joseph W. Goodman Book Writing Award, which recognizes recent and influential books in the field of optics and photonics that have contributed significantly to research, teaching, business, or industry.


Book Details

Date Published: 16 April 2012
Pages: 748
ISBN: 9780819487872
Volume: PM210

Table of Contents
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List of Acronyms

1. Introduction
1.1 Hyperspectral Remote Sensing
1.2 Elements of Hyperspectral Sensing
      1.2.1 Material spectroscopy
      1.2.2 Radiative transfer
      1.2.3 Imaging spectrometry
      1.2.4 Hyperspectral data processing
1.3 Examples of Remote Sensing Applications
1.4 Summary

2. Optical Radiation and Matter
2.1 Propagation of Electromagnetic Radiation
      2.1.1 Propagation in free space
      2.1.2 Propagation in dense media
      2.1.3 Plane waves in dense media
2.2 Complex Index of Refraction
      2.2.1 Relationship with the complex dielectric constant
      2.2.2 Lorentz oscillator model
      2.2.3 Drude theory of strong conductors
2.3 Propagation Through Homogenous Media
2.4 Reflection and Transmission at Dielectric Interfaces
2.5 Reflection and Transmission at Conductor Interfaces
2.6 Radiometry
      2.6.1 Point sources
      2.6.2 Lambertian sources
      2.6.3 Spherical scatterers
2.7 Propagation Through Scattering Media
      2.7.1 Mie scattering theory
      2.7.2 Rayleigh scattering theory
2.8 Summary
2.9 Further Reading

3. Atomic and Molecular Spectroscopy
3.1 Quantum Mechanics
      3.1.1 Stationary states of a quantum mechanical system
      3.1.2 Interaction with electromagnetic radiation
      3.1.3 Born-Oppenheimer approximation
3.2 Electromagnetic Absorption and Emission
      3.2.1 Einstein coefficients
      3.2.2 Line broadening
3.3 Electronic Spectroscopy of Atoms
      3.3.1 Single-electron atoms
      3.3.2 Polyelectronic atoms
3.4 Rotational Spectroscopy of Molecules
3.5 Vibrational Spectroscopy of Molecules
      3.5.1 Diatomic molecules
      3.5.2 Polyatomic molecules
3.6 Electronic Spectroscopy of Molecules
3.7 Summary
3.8 Further Reading

4. Spectral Properties of Materials
4.1 Apparent Spectral Properties
      4.1.1 Homogenous absorbing layer
      4.1.2 Opaque scattering layer
      4.1.3 Transparent scattering layer
      4.1.4 Multiple absorbing layers
      4.1.5 Multilayer dielectric thin films
      4.1.6 Rough-surface reflectance
      4.1.7 Emissivity and Kirchhoff's law
4.2 Common Remote Sensing Materials
      4.2.1 Atmospheric gases
      4.2.2 Liquid water
      4.2.3 Vegetation
      4.2.4 Minerals
      4.2.5 Soils
      4.2.6 Road materials
      4.2.7 Metals
      4.2.8 Paints and coatings
4.3 Summary
4.4 Further Reading

5. Remotely Sensed Spectral Radiance
5.1 Radiative Transfer Modeling
      5.1.1 Atmospheric modeling
      5.1.2 Moderate-resolution atmospheric transmission and radiation
      5.1.3 Atmospheric path transmission
      5.1.4 Atmospheric path radiance
      5.1.5 Downwelling radiance
5.2 Remote Sensing Models
      5.2.1 Facet model for a solid surface
      5.2.2 Gaseous effluent model
      5.2.3 Shallow-water model
5.3 Summary

6. Imaging System Design and Analysis
6.1 Remote Imaging Systems
6.2 Optical System Design
      6.2.1 Point spread function
      6.2.2 Optical aberrations
      6.2.3 Modulation transfer function
      6.2.4 Lens design
6.3 FPA Materials and Devices
      6.3.1 Quantum detectors
      6.3.2 Photoconductors
      6.3.3 Photodiodes
      6.3.4 Detector materials
      6.3.5 Detector noise
      6.3.6 Detector performance
6.4 Radiometric Sensitivity
      6.4.1 Signal and background radiance
      6.4.2 Focal plane irradiance
      6.4.3 Photoelectronic conversion
      6.4.4 Total system noise
      6.4.5 Total system performance
6.5 Spatial Sampling
6.6 Spatial Resolution
      6.6.1 Ground-resolved distance
      6.6.2 System modulation transfer function
6.7 Image Quality
6.8 Summary
6.9 Further Reading

7. Dispersive Spectrometer Design and Analysis
7.1 Prism Spectrometers
      7.1.1 Prism dispersion
      7.1.2 Prism spectrometer design
7.2 Grating Spectrometers
      7.2.1 Grating diffraction
      7.2.2 Grating spectrometer design
7.3 Imaging Spectrometer Performance
      7.3.1 Spatial and spectral mapping
      7.3.2 Spatial and spectral response functions
      7.3.3 Radiometric sensitivity
7.4 System Examples
      7.4.1 Airborne Visible/Infrared Imaging Spectrometer
      7.4.2 Hyperspectral Digital Imagery Collection Experiment
      7.4.3 Hyperion
      7.4.4 Compact Airborne Spectral Sensor
      7.4.5 Spatially Enhanced Broadband Array Spectrograph System
      7.4.6 Airborne Hyperspectral Imager
7.5 Summary

8. Fourier Transform Spectrometer Design and Analysis
8.1 Fourier Transform Spectrometers
      8.1.1 Interferograms
      8.1.2 Spectrum reconstruction
      8.1.3 Spectral resolution
      8.1.4 Spectral range
      8.1.5 Apodization
      8.1.6 Uncompensated interferograms
8.2 Imaging Temporal Fourier Transform Spectrometers
      8.2.1 Off-axis effects
      8.2.2 Additional design considerations
8.3 Spatial Fourier Transform Spectrometers
8.4 Radiometric Sensitivity
      8.4.1 Signal-to-noise ratio
      8.4.2 Noise-equivalent spectral radiance
      8.4.3 Imaging spectrometer sensitivity comparison
8.5 System Examples
      8.5.1 Field-Portable Imaging Radiometric Spectrometer Technology
      8.5.2 Geosynchronous Imaging Fourier Transform Spectrometer
      8.5.3 Spatially Modulated Imaging Fourier Transform Spectrometer
      8.5.4 Fourier Transform Hyperspectral Imager
8.6 Summary

9. Additional Imaging Spectrometer Designs
9.1 Fabry-Perot Imaging Spectrometer
9.2 Acousto-optic Tunable Filter
9.3 Wedge Imaging Spectrometer
9.4 Chromotomographic Imaging Spectrometer
      9.4.1 Rotating direct-view prism spectrometer
      9.4.2 Multi-order diffraction instrument
9.5 Summary

10. Imaging Spectrometer Calibration
10.1 Spectral Calibration
      10.1.1 Spectral mapping estimation
      10.1.2 Spectral calibration sources
      10.1.3 Spectral-response-function estimation
      10.1.4 Spectral calibration example
10.2 Radiometric Calibration
      10.2.1 Nonuniformity correction of panchromatic imaging systems
      10.2.2 Radiometric calibration sources
      10.2.3 Dispersive imaging spectrometer calibration
      10.2.4 Imaging Fourier transform spectrometer calibration
10.3 Scene-based Calibration
      10.3.1 Vacarious calibration
      10.3.2 Statistical averaging
10.4 Summary

11. Atmospheric Compensation
11.1 In-scene Methods
      11.1.1 Empirical line method
      11.1.2 Vegetation normalization
      11.1.3 Blackbody normalization
      11.1.4 Temperature/emissivity separation
11.2 Model-based Methods
      11.2.1 Atmospheric Removal Program
      11.2.2 Fast line-of-sight atmospheric analysis of spectral hypercubes
      11.2.3 Coupled-subspace model
      11.2.4 Oblique projection retrieval of the atmosphere
11.3 Summary

12. Spectral Data Models
12.1 Hyperspectral Data Representation
      12.1.1 Geometrical representation
      12.1.2 Statistical representation
12.2 Dimensionality Reduction
      12.2.1 Principal-component analysis
      12.2.2 Centering and whitening
      12.2.3 Noise-adjusted principal-components analysis
      12.2.4 Independent component analysis
      12.2.5 Subspace model
      12.3.6 Dimensionality estimation
12.3 Linear Mixing Model
      12.3.1 Endmember determination
      12.3.2 Abundance estimation
      12.3.3 Limitations of the linear mixing model
12.4 Extensions of the Multivariate Normal Model
      12.4.1 Local normal model
      12.4.2 Normal mixture model
      12.4.3 Generalized elliptically contoured distributions
12.5 Stochastic Mixture Model
      12.5.1 Discrete stochastic mixture model
      12.5.2 Estimation algorithm
      12.5.3 Examples of results
12.6 Summary
12.7 Further Reading

13. Hyperspectral Image Classification
13.1 Classification Theory
13.2 Feature Extraction
      13.2.1 Statistical separability
      13.2.2 Spectral derivatives
13.3 Linear Classification Algorithms
      13.3.1 k-means algorithm
      13.3.2 Iterative self-organizing data analysis technique
      13.3.3 Improved split-and-merge clustering
      13.3.4 Linear support vector machine
13.4 Quadratic Classification Algorithms
      13.4.1 Simple quadratic clustering
      13.4.2 Maximum-likelihood clustering
      13.4.3 Stochastic expectation maximization
13.5 Nonlinear Classification Algorithms
      13.5.1 Nonparametric classification
      13.5.2 Kernel support vector machine
13.6 Summary
13.7 Further Reading

14. Hyperspectral Target Detection
14.1 Target Detection Theory
      14.1.1 Likelihood ratio test
      14.1.2 Multivariate normal model
      14.1.3 Generalized likelihood ratio test
14.2 Anomaly Detection
      14.2.1 Mahalanobis distance detector
      14.2.2 Reed-Xiaoli detector
      14.2.3 Subspace Reed-Xiaoli detector
      14.2.4 Complementary subspace detector
      14.2.5 Normal mixture model detectors
14.3 Signature-Matched Detection
      14.3.1 Spectral angle mapper
      14.3.2 Spectral matched filter
      14.3.3 Constrained energy minimization
      14.3.4 Adaptive coherence/cosine estimator
      14.3.5 Subpixel spectral matched filter
      14.3.6 Spectral matched filter with normal mixture model
      14.3.7 Orthogonal subspace projection
14.4 False-Alarm Mitigation
      14.4.1 Quadratic matched filter
      14.4.2 Subpixel replacement model
      14.4.3 Mixture-tuned matched filter
      14.4.4 Finite target matched filter
      14.4.5 Least-angle regression
14.5 Matched Subspace Detection
      14.5.1 Target subspace models
      14.5.2 Subspace adaptive coherence/cosine estimator
      14.5.3 Joint subspace detector
      14.5.4 Adaptive subspace detector
14.6 Change Detection
      14.6.1 Affine change model
      14.6.2 Change detection using global prediction
      14.6.3 Change detection using spectrally segmented prediction
      14.6.4 Model-based change detection
14.7 Summary
14.8 Further Reading



Hyperspectral imaging is an emerging field of electro-optical and infrared remote sensing. Advancements in sensing and processing technology have reached a level that allows hyperspectral imaging to be more widely applied to remote sensing problems. Because of this, I was asked roughly six years ago to serve as an adjunct faculty member at the Air Force Institute of Technology in Ohio to construct and teach a graduate course on this subject as part of their optical engineering program. As I searched for a suitable textbook from which to teach this course, it became apparent to me that there were none that provided the comprehensive treatment I felt the subject required. Hyperspectral remote sensing is a highly multidisciplinary field, and I believe that a student of this subject matter should appreciate and understand all of its major facets, including material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. There are many resources that suitably cover these areas individually, but none that are all inclusive. This book is my attempt to provide an end-to-end treatment of hyperspectral remote sensing technology.

I have been using this textbook in manuscript form to teach a one-quarter class at the graduate level, with Masters and Ph.D. students taking the course as an elective and subsequently performing their research in the hyperspectral remote sensing field. The amount of material is arguably too much to fit within a single quarter and would ideally be spread over a semester or two quarters if possible. The content of the book is oriented toward the physical principles of hyperspectral remote sensing as opposed to applications of hyperspectral technology, with the expectation that students finish the class armed with the required knowledge to become practitioners in the field; be able to understand the immense literature available in this technology area; and apply their knowledge to the understanding of material spectral properties, the design of hyperspectral systems, the analysis of hyperspectral imagery, and the application of the technology to specific problems.

There are many people I would like to thank for helping me complete this book. First, I would like to thank the Air Force Research Laboratory for their support of this endeavor, and my many colleagues in the hyperspectral remote sensing field from whom I have drawn knowledge and inspiration during the 15 years I have performed research in this area. I would like to thank all of my OENG 647 Hyperspectral Remote Sensing students at the Air Force Institute of Technology who suffered through early versions of this manuscript and provided invaluable feedback to help improve it. In particular, I owe great thanks to Joseph Meola of the Air Force Research Laboratory, who performed a very thorough review of the manuscript, made numerous corrections and suggestions, and contributed material to Chapters 10 and 14, including participating in useful technical discussions concerning nuances of signal processing theory. I am very grateful for thorough, insightful, and constructive reviews of my original manuscript performed by Dr. John Schott of the Rochester Institute of Technology and Dr. Joseph Shaw of Montana State University on behalf of SPIE Press, as well as Tim Lamkins, Dara Burrows, and their staff at SPIE Press for turning my manuscript into an actual book. Additionally, I would like to acknowledge the support of Philip Maciejewski of the Air Force Research Laboratory for performing vegetation spectral measurements, the National Aeronautics and Space Agency (NASA) for the Hyperion data, the Defense Intelligence Agency for the HYDICE data, John Hackwell and the Aerospace Corporation for the SEBASS data, Patrick Brezonik and the University of Minnesota for the lake reflectance spectra, Joseph Shaw of Montana State University for the downwelling FTIR measurements, Bill Smith of NASA Langley Research Center for the GIFTS schematic and example data, and others acknowledged throughout this book for the courtesy of using results published in other books and journals.

Finally, this book would not have been possible were it not for the help and support of my wife Michelle and daughters Maria and Katie, who provided great patience and encouragement during the many hours that their husband and father was preparing, typing, and editing this book instead of giving time to them and attending to other things around our home. Now that this immense undertaking is completed, I hope to make up for some of what was lost.

Michael T. Eismann
Beavercreek, Ohio
March 2012

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