Introductory Digital Image Processing Remote Sensing Perspective

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A--··-·:t6-&?5'3-Library of Congress Cataloging-in-Publication Data Jensen, John R. .

Introductory ,digital image processing: a remote sensing.perspective /John R Jensen. - 3rd ed. p,_cm. -(Prentice Hall series in geographic information science)

•·Includes bibliographic references and index. ISBN 0-13-145361-0

I. Remote sensuig. 2. Image Processing - Digital techniques. I. Title. II. Senes. · G70.4.J46 2005

62 L36'78-dc22

Executive. Editor: Daniel E. Kaveri~y

Editor in Chief, Science: John Challice

Ma.-Xeting Manager: Robin Farrar

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... ·;Edit0ri~1 A.Ssistant: M~et Ziegi,~r

Vice Pie5i_dellt and Director of Production and Manufacturing, ESM: David W. Riccardi ProduCti~n'Editor: Beth 'Lew

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October 200 I (courtesy of NASA Earth Observatory, August 13, 2002). For additional information see.Friedl, M. A", Mclver, D. K, Hodges,J._ C. F., Zhang, X. Y., Much0ney, D., Strahler,

A.· H., Woodcock, C. E.;Gopal, S., Schneider, A., Coopei, A., BaCcini, A., Gao, F. and C. Schaaf,_ 2002, "Glohal Land Cover Mapping from MODIS: Algorithms and Early Results," Remote Sensjng oj Environment, 83(1~ . ·2):287-302.

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Digital Frame Camera Data Collection . ... 9()

Emerge, Inc., Digital Sensor System ... 98

Satellite Photographic Systems ... : ... : ... 98

Russian SPIN-2 TK-350 and KVR-1000 Cameras .... ... 98

US. Space Shuttle Photography . ... 101

Digital Image Data Formats ... IOI Band Interleaved by Pixel Format ...•.... 102

Band Interleaved by Line Format . ... I 02 Band Sequential Format ... ... 103

Summary .••...•...•...•...•... 103

References ...•.•... 104

Chapter 3-Digital Image Processing Hardware and Software Considerations •••••••• 107 Digital Image Processing System Considerations ... I 07 Central Processing Units, Personal Computers, Workstations, and Mainframes ... 108

Personaf.Computer ... :. _ ... 110

Computer Workstations . ... I 10 Mainframe Computers .. . . .. . . .. . . .. .. . . . ... I! 0 Read-Only-Memory, Random Access Memory, Serial 3.nd Pantllel Processing, and Arithmetic Coprocessor. ... I! 0 Read-Only Memory and Random Access Memory ... 113

Serial and Parallel Image Processing ... ; ... 113

Arithmetic Coprocessor ... 1 lJ Mode of Operation and Interface ... : ... ll 3 Mode of Operation ... 113

Graphical User Interface . , ... ll 4 Computer Operating System and Compiler(s) ... 1!5 Operating System ... ll 7 Compiler . ... ll 7 Storage and Archiving Considerations.: ... -... 117

Rapid Access Mass Storage ... '. ... ll 7 Archiving Considerations: Longevity'. ... , ... 1!8 Computer Display· Spatial and Color Resolution: ... ll 8 Computer Screen Display Resolutio!I . ... '. ... .- ... ll 8 Computer Screen Color Resolution •.... · ... -... 118

Important Image Processing Functions ... 120

Commercial and Public Digital Image Processing Systems ... 12_1 Digital Image Processing and the National Spatial Data Infrastructure ... 121

Sources of Digital Image Processing Systems ...•... 123

References ... 124

Chapter 4---lmage Quality Assessment and Statistical Evaluation ••••••••••••••••••• 127 Image Processing Mathematical Notation ... 127

Sampling Theory ... , ... , ... 128

The Histogram and Its Significance to Digital Image Processing ... 128

Image Metadata ... 132

Viewing Individual Pixel.Brightness Values at Specific Locations or within a Geographic Area ... ,, ... 132

Cursor Evaluation of Individual Pixel Brightness Values ...•...• 132

Two- and Three-dimensional Evaluation of Pixel Brighf1!ess Values within a Geo-graphic Area . ... 133

Univai:iate Descriptive Image Statistics ••••.• , .•• ; ... .-... ~ .•... 135

M.,eiiiure'p/Centrtil TendenqJnRemote Sensor Data .... :.,,.:, ,;,.,·.0

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135 •.

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CONTENTS l I 7 7 l )

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) ) l l l l l I 5 7 7 7 7 l l l l ) l l l

4

7 7 g g

z

z

z

l 5 ,.._, ~ 5 vii Measures of Dispersion . . . 135

Measures of Distribution (Histogram) Asymmetry and Peak Sharpness . ... 137

Multivariate Image Statistics . . . 137

Covariance in Multiple Bands of Remote Sensor Data . . . 138

Correlation between Multiple Bands a/Remotely Sensed Data ... 139

Feature Space Plots ... .-·141

Geostatistical Analysis ... -... 141

Relationships among Geostatistical Analysis, Autocorrelation, and Kriging .... 141

Calculating Average Semivariance . . . 143

Empirical Semivariogram .. . . 144

References ...•... 148

Chapter 5-Initial Display Alternatives and Scientific Visualization .•••••••••••••••• 151 Image Display Considerations ... , .. : ... 151

Black-and-White Hard-copy Image Display ... 154

Line Printer/Plotter Brightness Maps ... . . . 154

Laser or Ink-jet Printer Brightness Maps .... ...•... 154

Temporary Video Image j)isplay ... , ... 154

Black-and-White and Color Brightness Maps ... 154

Bitmapped Graphics ... 154

RGB Color Coordinate System ... ... 157

Color Look-up Tables: 8-bit .... ... 158

Color Look-up Tables: 24-bit .. ... !59

Color Composites ... . 161

Merging Remotely Sensed Data ... 164

Band Substitution ... 164

Colo:- Space Transformation and Substitution . . . 164

Principal Component Substitution . . . 168

Pixel-by-Pixel Addition of High-Frequency Informatian . ... 169

Smoothing Filter-based Intensity Modulation Image Fusion ... ... 169

Distance, Area, and Shape Measurement. ... , : ... 169

Distance Measurement. . . . .. . . . , ... · ... 169

Area Measuremeizt . . . 171

Shape Measurement ... : ... 172

References ... 172

Chapter 6--Electromagnetic :Radiation Principles and Radiometric Correction . ... 175

Electromagnetic Energy Interactions ... · ... 176

Conduction, Convection, and Radiation . ... · .... _ ... 176

Electromagnetic Radiation Models ... 176

Wave Model of Electromagnetic-Energy . ... -. . . 176

The Particle Model: Radiation from Atomic Structures .-... 181

Atmospheric Energy-Matter Interactions , ... 185

Refraction ... 185

Scattering ... 186

Absorption . ... · ... 188

Reflectance ... : . 190

Terrain- Energy..:.Matter Interactions ... ~ -... _ ... · ... 191

Hemispherical Reflectance, Absorptance,· and Transmittance .. · ... -.... 191

Radiant Flux Density .. '' .:

i . ... .

'!, ;c;' : . : .. '·" ...•... 192

Energy-Matter Interactions m;JieAtmosphere Once Again ...•. 194

Energy~Matter lnteritctioiis'ai

the'

Sensor System ... , ... , ... , ...•. , 194

Correctirig RemoteSensblg'i\)istem DetectorEiTor,,., ...•... , ... :194

-Random Bad Pixels (Shot Noise) ... 195

Line or Column Drop-outs ... 195

Partial Line or Column Drop-outs ... 195

Line-start Problems ... _ ... · ... . 197

N-line Striping . ... 198

Remote Sensing Atmospheric Correction ... 198

Unnecessary Atmospheric Correction ... 198

Necessary Atmospheric Correction ... 202

Types of Atmospheric Correction ... 202

Absolution Radiometric Correction of AJmospheric Attenuation . ... 203

Relative Radiometric Correction of Atmospheric Attenuation . ... 213

Correcting for Slope and Aspect Effects ... 220

The Cosine Correction ... 221

The Minnaert Correction ...•... 221

A Statistical-Empirical Correction . ... 222

The C Correction ... 222

References ... 222

Chapter 7---Geometr~c Correction . ... ~ ... 227

Internal and External Geometric Error ...•... 227

Internal Geometric Error ... 227

External Geometric Error .. ... 232

Types of Geometric Correction ... .' ... 234

Image-to-Map Rectification ... 235

lmage-to-lniage Registration ... 236

Hybrid Approach to Image Rectification/Registration . ... 236

Image-to-Map Geometric Rectification Logic ... 236

Example of Image-to-Map Rectification ... 244

Mosaicking ... , ... 250

Mosaicking Rectified Images .•.•... 250

References ... _ ...•...•... : ..•... 252

Chapter 8-Image Enhancement. ••••••••••••••••••••••••••••••••.•••••••..•.. 255

Image Reduction and Magnification ... 255

Image Reduction . ...•...•... 255

Image Magnification: ...••...•...•... 256

Transects (Spatial Profiles) ...••... _ ..••.•.••.•...•... 257

Spectral Profiles ... , ... 262

Contrast Enhancement ...•... 266

Linear Contrast Enhancement ... 266

Nonlinear ·Contrast Enhancement ... 272

Band Ratioing ... : ... : 274

Spatial Filtering ... 276

Spatial Convolution Filtering . ... 276

The Fourier Transform ...••..•...•..•... 287

Principal Components Analysis ..•...• · ... 296

Vegetation Transformations (Indices) ....•••... 30 I Dominant Factors Controlling Leaf Reflectance ...•....••...•.. 30 I Vegetation Indices . ....•.... , .•..•...•....••....•... 310

Texture Transformations ... , .•... , ...•.•....•••..• 322

First-order Statistics in the Spatial Do"main .. · ... _-... 322

-8econd,order-Statistics in the Spatial Domain

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I

CONTENTS ix

Fractal Dimension as a Measure of Spatial Complexity or Texture . ... 327

Texture Statistics Based on the Semi-variogram . ... 329

References ... 329

Chapter 9-Thematic Information Extraction: Pattern Recognition ... 337

Supervised Classification ... '. ... 338

Land-use and Land-cover Classification Schemes ... 340

Training Site Selection and Statistics Extraction ... 350

Selecting the Optimum Bands/or Image Classification: Feature Selection . ... 356

Select the Appropriate Classification Algorithm ... 370

Unsupervised Classification ... 379

Unsupervised ClassifiCation Using the Chain Method .. ... 379

Unsupervised Classification Using the JSODATA Method . ... 383

Unsupervised Cluster Busting ... 385

Fuzzy Classification ... 389

Classification Based on Object-oriented Image Segmentation ... 393

Object-oriented Image Segmentation and Classification . ... 393

Object-oriented Considerations-:· . ... : .. ; ... 399

Incorporating Ancillary Data in the Classification Process ... 399

Problems Associated vvith Ancillary Data ... 399

Approaches to Incorporating Ancillary Data to Improve Remote Sensing Classification Maps ... 399

References ... 40 I Chapter IO-Information Extraction Using Artificial Intelligence ••••••.••..••••..•• 407

Expert Systems ... 408

Expert System User lnteiface ... 408

Creating the Knowledge Base ... 408

Inference Engine ... : ... 413

On-line Databases ... .413

Expert Systems Applied to Remote Sensor Data .. ... 413

Advantages of Expert Systems ... ' .. 419

Neural Networks ...•... 421

Components and Characteristics of a Typical Artificial Neural 1Vetwork Used to Extract Information from Remotely Sensed Data . ... 421

Advantages of Artificial Neural Networks ... 425

Limitations of Artificial Neural Networks ... 426

Neural Networks versus Expert Systems Developed Using Machine Learning . .. 426

References ... 427

-Chapter 11-Thematic Information Extraction: Hyperspectral Image Analysis ••.•.•• 431 Multispectral versus Hyperspectral Data Collection ... , ... 431

Steps to Extract Information from Hyperspectral Data ... 433

NASA"s Airborne Visible/Infrared Imaging Spectrometer ... 435

Subset Study Area from Flight Line(s) •... .435

Initial Image Quality Assessment ... 435

Visual Individual Band Examination ... 435

Visual Examination of Color Composite Images Consisting of Three Bands . .... 437

Animation ... 437

Statistical Individual Band Examination ... : ... 437

Radiometric Calibration ... .438

In Situ Data Collection . ... 438

x

CONTENTS

I

Radiative Transfer-based Atmospheric Correction ... _ ... 438

Band-by-Band Spectral Polishing ... 441

Empirical Line Calibration Atmospheric Co1Tection . ... 443

Geom~tric Correction of Hyperspectral Remote_ Sensor Data ... 44 3 Reducing the Dimensionality ofHyperspectral Data ... .443

Minimum Noise Fraction Transformation ... 444

Endrnember Determination: Locating the Spectrally Purest Pixels ... 445

Pixel Purity Index Mapping ... 445

n-dimensional Endmember Visualization ... 44 7 Mapping and Matching Using Hyperspectral Data ... 450

Spectral Angle Mapper . ... 450

Subpixel Classification (Linear Spectral Unmixing) ... 453

Spectroscopic Library Matching Techniques ... 456

Indices Developed for Hyperspectral Data ... 457

Normalized Difference Vegetation Index- NDVI ... 457

Narrow-band Derivative-based Vegetation Indices ... 459

Yellowness Index-YI ... 459

Pi.'jsio!c:;~ical Reflectance Index-PRI ... 460

Nor.nullzed Difference Water Index-NDWI . ... 460

Red-edge Position Detennination - REP ... 460

Crop Chlorophyll Content Prediction ... 461

Derivative Spectroscopy .... , ... 461

References ... 462

Chapter 12-Digital Change Detection ••••••••••••••••••••••••••••••••••••.•••• 467 _{:_1} Steps Required to Perform Change Detection ... 467

Change Detection Geographic Region of Interest ... 467

Change Detection Ti.me Period . ... 467

Select an Appropriate Land-use/Land-cover Classification System . ... 468

Hard and Fuzzy Change Detection Logic . ... 468

Per-pixel or Object-oriented Change Detection .. ... 468

Remote Sensing System Considerations ... 468

Environmental Considerations of Importance When Performing Change Detection ... , ... 4 71 Selection of a Change Detection Algorithm ... 4 7 4 Change Detection Using Write Function Memory Insertion ... · ... 475

Multidate Composite Image Change Detection ... 475

Image Algebra Change Detection . ... 478

Post-classffication Comparison Change Detection ... 482

Change Detection Using a Binary Change Mask Applied to Date 2 ... 483 ·

Change Detection Using an Ancillary Data· Source as Date 1 . ... 483

Spectral Change Vector Analysis ... 484

Chi-square Transformation Change Detection .... ... 486

Cross-correlation Change Detection ... 486

Knowledge-based Vision Systems for Detecting Change ...•... 487

Vis!:al On-screen Change Detection and Digitization ... 487

Atmospheric Correction for Change Detection ... 491

WhenAimospheric Correction Is Necessary ... . 491

When Atmospheric Correction Is Unnecessary ... 492

Summary ...•...•...•... .492

I

CONTENTS xi

Chapter 13-Thematic Map Accuracy Assessment .••••••••...•••••••••••••.••.•• 495

Land-use and Land-cover Map Accuracy Assessment ... 495

Sources of Error in Remote Sensing-derived Thematic Products ... , ... 496

The Error Matrix . . . 499

Training versus Ground Reference Test Information ... 500

Sample Size . . . 501

Sample Size Based on Binomial Probability Theory ... 501

Sample Size Based on Multinomial Distribution ... 501

Sampling Design (Scheme) . . . 502

Simple Random Sampling . ... 504

Systematic Sampling . ... ." ... 504

Stratified Random Sampling ... 504

Stratified Systematic Unaligned Sampling ... 504

Cluster Sampling . ... 505

Obtaining Ground Reference Information at Locations Using a Response Design ... 505

Evaluation of Error Matrices . . . 505

Descriptive Evaluation of Error Matrices ... 505

Discrete Multivariate£! ~1a~vtlcal Techhi(fues Applied to the Error Matrix ... 506

Fuzzification of the Error Matrix ... 508

Geostatistical Analysis to Assess the Accuracy of Remote Sensing-derived Information ... , ... 512

Image Metadata and Lineage Information for Remote Sensing-derived Products ... 512

Individual Image Metadata ... 513

Lineage of Remote Sensing-derived Products ... 513

References . . . 513

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xiv

The book is organized according to the general flow or method by which digital remote sensor data are analyzed. Novices in the field can use the; book as a manual as they

perform the various functions associated with a remote

sensing digital image processing project.

The third edition has been revised substantially. The follow-ing summary indicates significant changes in each chapter. Chapter 1: Remote Sensing and Digital Image Process-ing. A revised introduction summarizes the remote sensing process. The various elements of the remote sensing process are reviewed, including statement of the problem, data col-lectiOn (in situ and remote sensing), data-to-information conversion, and information presentation alternatives. A ta.Xonomy of models used in remote sensing, geograrhic information systems· (GIS), and ?nvironmental research is included based on the method ofpfoces~;ing (deterministic, stochastic) or type of logic (inductive, deductive). The chap-ter concludes with an overview of the content of the book. Chapter 2: Remofo Sensing Data Collection. Analog (hard-copy) image-digitization is presented with imp1oved examples. Information-on recent satellite and aircraft remote sensing systems is presented, including:

Landsat Enhanced Thematic Mapper Plus (ETM+) SPOT 4 High Resolution Visible (HRV) and SPOT 5 High Resolution Visible Infrared (HRVIR) and Vegetation sensors

NASA Earth Observer (E0-1) sensors: Advanced Land Imager (ALI), Hyperion hyperspectral sensor, and LEISA atmospheric corrector

recent NOAA Geostationary Operational Environmental Satellite (GOES) and Advanced Very High Resolution Radiometer (AVHRR) sensor systems

ORBIMAGE, Inc., and NASA Sea-viewing Wide Field-of-View Sensor (Sea WiFS)

Indian Remote Sensing (IRS) satellite sensors

NASA Terra and Aqua sensors: Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER), Multiangle Imaging Spectroradiometer (MISR), and Moderate Resolution Imaging Spectrometer(MODIS) • high-spatial-resolution satellite remote sensing systems:

IK.ONOS (Space Imaging), QuickBird (DigitalGlobe),

PREFACE

OrbView-3 (ORBIMAGE), and EROS Al (ImageSat International)

suborbital hyperspectral sensors such as NASA's Airborne

Visible/Infrared Imaging Spectrometer (AVIRIS) and the Compact Airborne Spectrographic Imager 3 (CASI 3) digital frame cameras such as the EMERGE, Inc., Digital Sensor System (DSS)

satellite photographic systems such as the Russian SPIN-2 TK-350 and KVR-1000 cameras

There is a new discussion of remote sensing data formats (band interleaved by pixel, band interleaved by line, and band sequential).

Chapter 3: Digital Image Processing Hardware and Soft-ware Considerations. The most important hardSoft-ware_and_ software- conSiderations necessary to configure a quality remote sensing digital image processing system are updated. A historical review of the Intel, Inc., central processing unit (CPU) identifies the numbeI of transistors and millions of instructions per second (MIPS) that could. be processed through the years. New information on serial versus parallel image processing, graphical user interfaces, and the longev.., ity of remote sensor data storage media are presentod. The most important digital image processing functions found in a quality digital image processing system are updated. The functionality of numerous commercial and public do1nain digital image processing systems is presented along with rel-evant Internet addresses.

Chapter 4: Image Quality Assessment and Statistical . Evaluation. This chapter provides fundamental information on univariate and tnultivariate statistics that __ are routinely extracted from remotely sensed data. It includes new infor-mation on the importance of the histogram to digital image processing, image metaf!ata, and h01v tO view pixel bright-ness values at individual locations or within geographic areas. Methods of viewing individual bands of imagery in three dimensions are examined. Two-dimensional feature space plot logic is introduced. Principles of geostatistical · analysis are presented including spatial autocorrelation and the calculation of the empirical semivariogram.

Chapter 5: Initial Display Alternatives and Scientific Visualization. The concept of scientific visualization ·is introduced. Methods of visualizing data in both black-and-white. and color are presented with an improved discussion of color look-up table and color space theory. New informa-tion about bitrnapped images is provided. The Sheffield

I

PREFACE

Index is introduced as an alternative method for selecting the optimum bands when creating a color composite. Emphasis is placed_on how to merge different types of remotely sensed data for visual display and analysis using color space trans-formations, including new material on the use of the chro-maticity color coordinate system and the Brovey transform. The chapter concludes with a summary of the mathematics necessary to calculate distance, area, and shape measure-ments on rectified digital remote sensor data.

Chapter 6: Electromagnetic Radiation Principles and Radiometric Correction. This significantly revised chapter deals with radiometric correction of remote sensor data. The first half of the chapter reviews electromagnetic radiation models, atmospheric energy-matter interactions, terrain energy-matter interactions, and sensor system energy-mat-ter inenergy-mat-teractions. Fundamental radiometric concepts are then introduced. Various methods of correcting sensor detector-induced error

in

remotely sensed images are presented. Reinote sensing atmospheric correction is then introduced, including a discussion of when it is necessary to atmospher-ically correct remote sensor data. Absolute radiometric cor-rection alternatives based on radiative transfer theory are introduced. Relative radiometric correction of atmospheric attenuation is presented. The chapter concludes with meth-ods used to correct for the eff~cts ofterr.ain slope and aspect.

Chapter 7: Geometric Correction. The chapter contains new ir..formation about image offset (skew) caused by Earth rot&.tion and how skew can be corrected. New information is introduced about the geometric effects of platform roll, pitch, and yaw Caring remote sensing data acquisition. The chapter then concentrates on image:..to-image registration and image-to-map rectification. New graphics and discus-sion make clear the distinction bef"'.:veen GIS-related input-to-output (forward) mapping logic and output-to-input (inverse) mapping logic required to resample and rectify ras-ter remote sensor data. The chapras-ter concludes with a new section on dig!!?.! mosaicking using feathering logic. Chapter 8: Image Enhancement. New graphics and text describe how spatial profiles (transects) and spectral profiles are extracted from multispectral and hyperspectral imagery. Piecewise linear contrast stretching is demonstrated using new examples. The use of the Fourier transform to remove striping in remote sensor data is introduced. An updated review of vegetation transformations (indices) is provided. It includes fundamental principles associated with the domi-nant factors controlling leaf reflectance and many newly developed indices. Texture measures based on conditional variance detection and the geostatistical semivariogram are discussed.

Chapter 9: Thematic Information Extraction: Pattern Recognition. The chapter begins with an overview of hard

versus fuzzy land-use/land-cover classification logic. It then delves deeply into supervised classification. It introduces several new land-use/land-cover classification schemes. It includes a new section on nonparametric nearest-neighbor classification and a more in-depth treatment of the maxi-mum-likelihood classification algorithm based on

probabil-ity density functions. Unsupervised classification using ISODATA is made easier to understand using an additional empirical example. There is a new section on object-oriented image segmentation and how it

can

be used for image classi-fication. The chapter concludes by updating methods to incorporate ancillary data into the remote sensing classifica-tion process.

Chapter 10: Information Extraction Using Artificial Intelligence. This iieW-'tfl~pter begins by briefly reviewing the history of artificial intelligence. It then introduces the concept of an expert system, its components, the kno\vledge representation process, and the inference engine. Human-derived rules are then input to a rule-based expert system to extract land-cover information from remote sensor data. In a separate example, the remote sensor data are subjected to machine learning to demonstrate how the rules used in an expert system can be developed with minima! human inter-vention. The use of artificial neural networks in remote sens-ing classification is introduced. The chapter concludes with a discussion of the advantages and limitations of expert sys-tems and artificial neural networks for information extrac-tion.

Chapter 11: Thematic Information Extraction: Hyper-spectral Image Analysis. This new chapter begins by reviewing the ways hyperspectral data are collected. It then uses an empirical case study based on AVIRIS data to intro-duce the general steps to extract information from hyper-spectral data. Emphasis is on rad.iative transfer-based radiometric correction of the hyperspectral data, reducing its dimensionality, and extracting relevant endmembers. Vari-ous methods of mapping and matching are then presented including the spectral angle mapper,.linear spectral unmix-ing, and spectroscopic library matching techniques. _The chapter concludes with a summary of various narrow-band indices that can be used with hyperspectral data.

Chapter 12: Digital Change Detection. The change detec-tion flow chart summarizes current methods. New examples of write function memory insertion, multiple-date composite image, and image algebra (image differencing) change detection are ·provided. New chi-square transformation and cross-correlation change detection methods are introduced.

xvi

The chapter concludes with a discussion ab< Jut when

it

is necessary to atmospherically correct remote sensor data for change detection applications.

Chapter 13: Thematic Map Accuracy Ass,,ssment. This new chapter begins by reviewing the approaches to land-use/land-cover classification map accuracy iissessment. It then suminarizes the sources of error in ret:l.ote sensing-derived thematic map products. Various methods of comput-ing the sample size are introduced. Samplcomput-ing designs (schemes) are discussed. The evaluation of error matrices usirig descriptive and discrete multivariate analytical tech-niques is presented. A section describes hov' ~o incorporate fuzzy information into an accuracy assessment. The chapter concludes with observations about geostatistical measures

Used in accuracy assessment. Acknowledgm0r.t;

The author thanks the following individuals for their support and assistance in the preparation of the third edition. Ryan Jensen contributed to the survey of digital image processing systems in Chapter 3. Kan He, Aueqiao Huang, and Brian Hadley provided insight for the radiometric correction and Fourier transform analysis sectioris in Chapter 6. David

PREFACE

Vaughn contributed to the vegetation index section in Chap-ter 8. Jason Tullis and Xueqiao Huang contributed to the use of artificial intelligence in digital image processing in Chap-. ter l 0Chap-. Anthony MChap-. Filippi contributed to information

extraction using hyperspectral data in Chapter 11. Russ Con-galton provided insight and suggestions for assessing the-matic map classification accuracy in Chapter 13. Lynette Likes provided computer network support. Maria Garcia, Brian Hadley, George Raber, Jason Tullis, and David Vaughn assisted with proofreading. Finally, I would like to especially thank my wife, Marsha, for her help and unwaver-ing encouragement.

John R. Jensen University of South Carolina

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-ment quantitatively the demographic characteristics of the population. These in situ data are then used to accept or reject hypotheses associated with human activities and socioeco-nomic characteristics.

Conversely, a scientist might elect to place a transducer at the study site to make measurements. A transducer is a device that converts input energy of one form into output energy of another form. Transducers are usually placed in direct physical contact with the object of int~rest. Many types of transducer are available. For example, a scientist could use a thermometer to measure the temperature of the air, soil, or water; an anemometer to measure the speed of the wind; or a psychrometer to measure humidity. The data might be recorded by the transducers as an analog electrical signal with voltage variations related to the intensity of the property being measured. Often these analog signals are transformed into ...-r~-:i•al values using analog-to-digital (A-to-D) conversion proc._.Gures. Jn situ data collection using trans-ducers relieves tl1e scientist of monotonous data collection duties in inclement weather. Also, the scientist can distribute the transducers at important geographic locations throughout the study area, allowing the same type of measurement to be obtained at many locations at the same instant in time. Some-times data from the transducers are telemetered electroni-cally to a central receiving station for rapid evaluation and archiving.

T\VO types of in situ data collection often used in support of

remote sensing investigations are depicted in Figure 1-2. In the first example, spectral reflectance measurements of smooth cordgrass (Spartina alterniflora) in Murrells Inlet, South Carolina, are being recorded in the blue, green, red and near-infrared portions of the electromagnetic spectrum ( 400----1100 run) using a hand-held spectroradiometer (Figure l-2a). Spectral reflectanr;e measurementS obtained in the field can be used to calibrate spectral reflectance measurements col-lected by a remote sensing system located on an aircraft or satellite. A scientist is obtaining precise x,y,z geographic coordinates of an in situ sample location using a global posi-tioning system (GPS) in Figure l-2b.

In Situ Data-Collection Error

Many people believe that in situ data are absolutely accurate because they were obtained on the ground. Unfortunately, error can also be introduced during in situ data collection. Sometimes the scientist is an intrusive agent in the field For example, in Figure 1-2a the :si.:ientist's shadow or the ladder shadow could fall within the instantaneous-field-of-view

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about an object or phenomenon within the instanta-neous-field-of view (IFOV) of the sensor-system

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platform.

(IFOV) of the handheld spectroradiometer, causing spectral reflectance measurement error. In addition, the scientists could accidently step on the area to be measured, compacting the vegetation and soil prior to data collection. Any of these activities would result in biased data collection.

Scientists could also collect data in the field using biased procedures often referred to as method-produced error. Such error can be introduced by:

a sampling design that does not capture all of the spatial variability of the phenomena under investigation (i.e.,

'

I

Remote Sensing Data Collection 3

In Situ

Data Collection

a. Spectroradiometer measurement. b. Global positioning sy-stem (GPS) measurement.

Figure 1-2 a) A scientist is collecting in situ spectral reflectance measurements of smooth cordgrass (Spartina alterniflora) in Murre11s Iulet, SC, using a handheld spectroradiometer located arproximately 2 m above the mudflat surface. The in situ spectral re-flectance measurements from 400 to 1100 nm can be used to calibrate the spectral rere-flectance measurements obtained from a remote sensing system on board an aircraft or satellite. b) A scientist is obtaining x,y,z geographic coordinates of a site using a global positioning system (GPS) capable of accuracies within± 50 cm.

sonie phenomena or geographic areas are oversample.d while others are undersampled);

operating in situ measurement--instruments improperly; or using an in situ meastirement instrument that has not been calibrated properly (or recently),

Intrusive in situ data collection, coupled with human method-produced error and measurement-device miscalibra-tion, all contribute to in situ data-collection error. Therefore, it is a misnomer to refer to in situ data as ground truth data. Instead, we should refer to it simply as in situ ground ~fer­

ence data, and acknowledge that it also contains error.

•

Remote Sensing Data Collection

Fortunately, it is also possible to collect certain types of information about an object or geographic area from a distant vantage point using remote sensing instruments (Figure 1-1 ).

The American Society for Photogramnietry and Remote Sensing (ASP RS) defines remote sensing as (Colweli, l 9S3):

the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study. In 1988, ASPRS adopted a combined formal definition of photogrammetry and remote sensing as (Colwell, l 997):

the art, science, and technology of obtaining reli-able information about physical objects and the environment, through the process of recording, measuring and interpreting imagery and digital rep-resentations of energy patterns derived from non-contact sensor systems.

Robert Green at NASA's Jet Propulsion Laboratory (JPL) suggests that the term remote measurement might be used instead of remote sensing because data obtained using hyper-spectraI- remote sensing systems are so accurate (Robbins,

Figure 1-3 An interaction model depicting the relationships of the mapping sciences (remote sensing, gecgraphic infonnation sy·stems, and cartography/surveying) as they relate to mathematics and logic and the physical. biological, and social sciences.

1999). Hyperspectral digital image processing is discussed in Chapter II.

ObseNations about Remote Sensing

The following brief discussion. foccses on various terms found in the formal definitions of remote sensing.

Is Remote Sensing a Science?

A science is defined as the broad field of human knowledge concerned with facts held together by principles (rules). Sci-entists discover and test facts and principles by the scientific method, an orderly system of solving problems. Scientists generally feel that any subject that humans can study by using the scientific method and other special rules of think-ing may be called a science. The sciences include I) mathe-matics and logic, 2) the physical sciences, such as physics and chemistry, 3) the biological sciences, such as botany and zoology, and 4) the social sciences, such as geography, soci-ology, and anthropology (Figure 1-3). Interestingly, some pt':rSons do not con.sider mathematics and IOgic to be sci-ences. But the fields of knowledge associated with mathe-matics and logic are such valuable tools-for science that we

cannot ignore them. The human race's earliest questions were concerned with "how many" and "what belonged together." They struggled to count, to classify, to think sys-tematically, and to describe exact_ly. In many respects, the state of development of a sc_ience is indicated by the use it makes of mathematics. A science seems to begin witll simple mathematics to measure, then works toward more complex mathematics to explain.

Remote sensing is a tool or technique similar to mathematics. Using sensors to measure the amount of electromagnetic radiation (EMR) exiting an object or geographic area from a distance and then extracting valuable information from the data using mathematically and statistically based algorithms is a scientific activity (Fussell et ·al., 1986). It functions in harmony with other spatial data-collection teChniq"J.es or tools of the mapping sciences, including cartography and geographic info•L1ation systems (GIS) (Curran, 1987;. Clarke, 2001). 0<.iiiberg and Jensen (1986) and Fisher and Lindenberg (1989) suggest a.model where there is interac-tion between remote sensing, cartography, and GIS; where no subdiscipline dominates; and all are recognized

as

having unique yet overlapping areas of knowledge and intellectual activity as they are used in physical, biological, and social science rosearch (Figure 1-3).

Is Remote Sensing ari Art?

The process of visual photo or image interpretation brings to bear not only scientific knowledge but all of the background that a person has obtained ~n his or her lifetime. Such learn-ing cannot be measured, programmed, or completely Under-stood. The synergism of combining scientific knowledge with real-world analyst experience allows th~ interpreter to develop heuristic rules of thumb to extract information from the imagery. Some image analysts are superior to_ other image analysts because they 1) understand ihe scientifiC principles better, 2) are more widely traveled and have seen many landscape objects and geographic areas, and/or 3) have the ability to synthesize scientific principles and real-world knowledge to reach logical and correct conclusions. Thus, remote sensing image interpretation is both an art and a sci-ence.

Information about an Object or Area

Sensors can be used to obtain very specific information about an object (e.g., the diameter of a cottonwood tree's crown) or the geographic extent of a phenomenon (e.g., the polygonal boundary of a cottonwood stand). The EMR reflected, emit-ted, or back-scattered from ,an object or geographic area is used as a surrogate for the actual property under

investiga-I

I

Reinote Sensing Data Collection

tion. The electromagnetic energy measurements muSt be cal--ibrated and turned into information using visual and/or digital image processing techniques.

The Instrument-(S~nsor)

Remote sensing is performed using an instrument, often referred to as a sensor. The majority of remote sensing instru-ments record EMR that travels at a velocity of 3 x 108 _{m s-}1

from the source, directly through the vacuum of space or indirectly by reflection or reradiation to the sensor. The EMR represents a very efficient high-speed communications link between the sensor and the remote phenomenon. In fact, we know of nothing that travels faster than the speed of light. Changes in the amount and properties of the EMR become, upon detection by the sensor, a valuable source of data for interpreting important properties of the phenomenon (e.g., temperature, color). Other types of force fields may be used in place of EMR, including sound waves (e.g., sonar). How-ever, the majority of remotely sensed data collected for Earth resource applications are the result of sensors that record electromagnetic energy.

How Far Is Remote?

Remote sensing occurs at a distance from the object or area of interest. Interestingly, there is no clear distinction about how greafthis distance should be. The distance could be 1 m, 100 m, or more than 1 million meters from the object or area of interest. Much of astronomy is based on remote sensing. In fact, many of the most innovative remote sensing systems and visual and digital image processing methods were origi-nally developed for remote sensing extraterrestrial land-scapes such as the moon, -Mars, lo, Saturn, Jupiter, -etc. This text, ho\\'ever, is concerned primarily with remote sensing of the terrestrial Earth, using sensors that are placed on subor-bital air-breathing aircraft or orsubor-bital satellite platforms placed in the vacuum of space.

Remote sensing and digital image processing techniques can also be used to analyze inner space. For example, an electron microscope can be used to obtain photographs of extremely small objects on the skin, in the eye, etc. Anx-ray instrument is _a remote sensing system where the skin and muscle are like the atmosphere that must be penetrated,· and the interior bone or other matter is often the object of interest. Many dig-ital image processing enhancement techniques presented in this text are Well suited to the analysis of "inner space" objeC!s.

5

Remote Sensing Advantages and Limitations

Remote sensing has several unique advantages as well as some limitations.

Advantages of Remote Sensing

Remote sensing is unobtrusive if the sensor passively records the electromagnetic eriergy reflected or emitted by the phe-nomenon of interest. Passive remote sensing does not disturb the object or area of interest.

Remote sensing devices are programmed to collect data sys-tematically, such as within a single 9 x 9 in. frame of vertical aerial photography or a single line of Systeme Probatoire d'Observation de la Terre (SPOT) image data collected using a linear array. This systematic data collection CTm--1'-emove the sampling bias introd-Uced in some in situ investigatiOns. Under controlled conditions, remote sensing can provide fundamental biophysical information, including x,y location, z elevation or depth, biomass, temperature, and moisture content. In this sense it is much like surveying, providing fundamental information that other sciences can use when conducting scientific investigations. However, unlike much of surveying, the remotely sensed data can be obtained sys-tematically over very large geographic areas rather than just single-point observations. In fact, remote sensing-derived information is now critical to the successful modeling of numerous natural (e.g., water-supply estimation; eut.rophica-tion studies; non!)oint _source poliueut.rophica-tion) and cultural (e.g., land-use conversion at the urban fringe; water-demand esti-mation; population estimation) processes (Walsh et ai., 1999; Stow et al., 2003). A good example is the digital elevation model that is so important in many spatialiy-distributed GIS _ models (Clarke, 2001). Digital elevation models are now produced almost exclusively from stereoscopic imagery, light detection and ranging (LIDAR), or radio .detection and ranging (RADAR) measurements.

Limitations of Remote Sensing

Remote sensing science has limitations. Perhaps the greatest · limitation is that it is ofterl oversold. Remote sensing is not a panacea that will provide all the infonnation needed to con-duct physical, biological, or social science research. It simply provides some spatial, spectral, and temporal information of value in a manner that we hope is effiCient and economical.

'

The Remote Sensing Process

Statemen·~

Data

the Proh!~ - ...,c.,,01,,1..,...,ti"o"nRIJ! • Formulate Hypothesis

(if appropriate)

•Select Appropr:ate Logic

- Inductive and/or - Deductive -Technological

•Select Appropriate Model

- Deterministi ·

-Empirical

- Knowledge-based - Process-based - Stochastic

•In SiLu Measurements

- Field (e.g., x,y,z from OPS,

biomass, spectroradiometer)

- Laboratory (e.g., reflectance, leaf area index)

• Collateral Data

- Digital elevation models

- Soil maps

- Surficial geology m.aps - Population density, etc.

• Remote Sensing - Passive analog - Frame camera - Videography - Passive digital - Frame camera - Scanners - Multispectral - Hyperspectral - Linear and area arrays

- Multispectral - H yperspectral -Active - Microwave (RADAR) - Laser (LIDAR) -Acoustic (SONAR)

Data-to-Information Conversion.,_ _ __,,., lnformation Presentation

• Analog (Visual) Image Processing - Using the Elements of

Image Interpretation

•Digital Image Processing

- Preprocessing - Radiometric Correction - Geometric Correction - Enhancement - Photogrammetric analysis - Parametric, such as - Maximum likelihood - Nonparametric, such as

- Artificial neural networks - Nonmetric, such as - Expert systems -Decision~tree classifiers - Machine learning - Hyperspectral analysis - Change detection - Modeling

- Spatial modeling using GIS data

- Scene modeling based on physics of energy/matter interactions - Scientific geovisualization

- l, 2, 3, and n dimensions

• Hypothesis Testing

- Accept or reject hypothesis

• Image Met.adata - Sources - Processing lineage •Accuracy Assessment -Geometric - Radiometric -Thematic - Change detection

•Analog and Digital

- Images - Unrectified - Orthoimages - Orthophotomaps - Thematic maps - GIS databases - Animations - Simulations • Statistics - Univariate - Mul!ivariate •Graphs - l, 2, and 3 dimensions

Figure 1-4 Scientists generally use the remote sensing process when extracting information from remotely sensed data .

Human beings select the most appropriate remote sensing system to collect the data, specify the various resolutions of the remote sensor data, calibrate the sensor, select the plat-form that will carry the sensor, d.eterrnine when the data will be collected, and specify how the data are processed. Human method-produced error may be introduced as the remote sensing instrument and mission parameters are specified. Powerful active remote sensor systems that emit their own electromagnetic radiation (e.g., LID AR, RADAR, SONAR) can be intrusive and affect the phenomenon being investi-gated. Additional research iS required to determine how intrusive these active sensors can be.

Remote sensing instruments may become uncalibrated, resulting in uncalibrated remote sensor data. Finally, remote sensor data may be expenSive to collect and analyze. Hope-fully, the information extracted from the remote sensor data justifies the expense.

• -Tile Remote Sensing Process

Urban planners (e.g., land use, transportation, utility) and natural resource managers \e.g., wetland, forest, grassland, rangeland) recognize that spatially distributed information is essential for ecological modeling and planning (Johannsen et al., 2003). Unfortunately, it is very difficult to obtain such information using in situ measurement for the aforemen-tioned reasons. Therefore, public agencies and scientists have expended significant resources in developing methods to obtain the required information using remote sensing sci-ence (Goetz, 2002; Nemani et al., 2003). The remote sensing data-collection and analysis procedures used for Earth resource applications are often implemented in a systematic fashion referred to as the remote sensing process. The

proce-du._~s in the remote sensing process are summarized here and in Figure I-4:

j

I

The Remote Sensing Process

The hypothesis to be tested is defined u; ing a specific type of logic (e.g., inductive, deductive) a)d an appropriate processing model (e.g., deterministic, st,·,chastic).

In situ and collateral data necessary to CJ.librate the remote sensor data and/or judge its geometric, radiometric, and thematic characteristics are collected.

Reffiote sensor data are collected passively or actively using analog or digital remote sensing instruments, ideally at the same time as the in situ data.

In situ and remotely sensed d.ata are

r

recessed using a) analog image processing, b) digital llaage processing, c) modeling, and d) n-dimensional visualization.

Metadata, processing lineage, and the accuracy of the information are provided and the results communicated using images, graphs, statistical tables, GIS databases, -Spatial Decision Support Systems (SDSS), etc.

It is useful to review the characteristics of these remote sens-ing process procedures.

Statement of the Problem

Sometimes the general public and even children look at

~.erial photography or other remote sensor data and extract useful information. They typically do this without a formal hypothesis in mind. More often than not, however, they inter-pret the imagery incorrectly because they do not understand the nature of the remote sensing system used to collect the data or appreciate the vertical or oblique perspective of the terrain recorded in the imagery.

Scientists who use remote sensing, on the other hand, are usually trained in the scientific method---a way of thinking about problems and solving them. They use a formal plan that has at least five elements: 1) stating the problem, 2) forming the research hypothesis (i.e., a possible explana-tion), 3) observing and experimenting, 4) interpreting data, and 5) drawing conclusions. It is not necessary to follow this formal plan exactly.

The scientific method is normally used in conjunction with environmental models that are based on two primary types of logic:

deductive logic inductive logic

7

Models based on deductive and/or inductive logic can be fur-ther subdivided according to whefur-ther they are processed detenninistically or stochastically. Table 1-1 summarizes the relationship ben.veen inductive and deductive logic and deterministic and stochastic methods of processing. Some-times information is extracted from remotely sensed images using neither deductive nor inductive logic. This is referred to as the use of technological logic.

Deductive Logic

When stating the remote sensing-related problem using deductive logic, a scientist (Skidmore, 2002):

draws .a specific conclusion from a set of general propositions (the premises). In other words, deduc-tive reasoning proceeds from general truths or rea-sons_ (where the premises are self-evident) to a conclusion. The assumption is that the conclusion necessarily follows the premises: that is, if you accept the premises, then it would be S;elf-contradictory to reject the conclusion (p. I 0).

For example, Skidmore suggests that the simple normalized difference vegetation index (NDVI):

NDVI

=

Pnir-Pred

Pnir + Pred

(1-1)

is a deduced relationship between land cover and the amount of near-infrared (Pnir) and red (Pred) spectral re~ectance.

Generally, the greater the amount of healthy green vegetation in the IFOV of the sensor, the greater the NDVI value. This relationship is deduced from the physiological fact that chlo-rophyll a and bin the palisade layer ofh_eiilthy green leaves absorbs most of the incident red radiant flux while the spongy mesophyll leaflayer reflects much of the near-infra-red radiant flux (refer to Chapter 8 for additional information aboµt vegetation indices).

Inductive Logic

Problem statements based on inductive logi.c reach a conclu-sion from particular facts or observations that are treated as evidence. This is the type of logic used most often in physi-cal, natural, and social science research. It usually relies heavily on statistical analysis. Building an inductive model normally involves defining the research (null) hypothesis, collecting the required data, conducting a statistical analysis, acceptance or rejection of the null hypothesis, and stating the level of confidence (probability) that should be associated with the conclusion.

Table 1-1. A taxonomy of models used in remote sensing, GIS, and environmental science research (adapted from Skidml•fe, 2002).

Deterministic Models

Deterministic models can be baSed on illductive or deductive logic or both. Deterministic models use very specific input and result in a fixed output unlike stochastic models that may include randomly derived input and output information. There are three general types of deterministic model: empir-ical, knowledge-driven, and process-driven.

!

Statistical models (e.g., bi-vari-:::\·a:tc regression relationship :_~>-'t;¢tWtcn NOVI'and biomaSs)

_~_>"'frqi~ing supeivised classifiers

~;'.-Thieshold ffiodels

May generate rules from train-ing data (e_.g., using' C5.0) Bayesian _inferenCe filgorithm

Sf:<ites thai the conditional prob-i··:_ability of 0a _hypothesis

occ_ur-:~:<ri~g is a __ function of the ,,; -:evidi:nce _;.

~~-:~ _syS_~s-

"'"'·'·,·•.

~_-;,M~_dific~~O~ Of inductive

:2.:.-iOOdet coc'fficiCnts fof. tociI

~t:. ~r:c::tions Using fi~-rd

Or

laOO:.

-->·:-Jatory dai-a

'·-·;_.

:,(~;~~··

,,

--:-<_

Neural n'etwork classification: . - trained based on induction

·_.:.'random weights may be. -, .assigned prior to first epoch

- (Ptoce.5Sing) ·

~< :'-Mo~te cifrt() siffiulatiOn ";- ,::

;

~:-Clds~ffi~*(w'n'

bY

Su~ claSsification tilg:Orithins

(e.k., :m~imUm likelihood

via modcf iii.Version) Modified inductive models ProccSS mOdcls ''. Expert S:Ysterii basCd on -knoW1ooge:b~e ofriiles

-cXtrabkd ·rrb_rii

art

exPert

- anaiySis 'b)'.·infef-enCe "engine

R~d~~logicil-~00els

::

Ecrilo~Cat Iri~ets·}-_~;--­ AimosphedC'ITIOdels -~

Monte Clirlo_:simulation

Empirical: An empirical model is based on ernpiricism-wherein a scientist should not accept a proposition unless he or she has witnessed the relationship or condition in the real world. Therefore; a deterministic. empirical model is founded on observations or measurements made in the real world or laboratory. Consequently, empirical models are usu-ally based on data extracted from site-specific, local study areas.