ERDAS Field Guide

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ERDAS Field Guide™

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Printed in the United States of America.

The information contained in this document is the exclusive property of ERDAS, Inc. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by ERDAS, Inc. All requests should be sent to the attention of:

Manager, Technical Documentation ERDAS, Inc.

5051 Peachtree Corners Circle Suite 100

Norcross, GA 30092-2500 USA.

The information contained in this document is subject to change without notice.

Government Reserved Rights. MrSID technology incorporated in the Software was developed in part through a project at the Los Alamos National Laboratory, funded by the U.S. Government, managed under contract by the University of California (University), and is under exclusive commercial license to LizardTech, Inc. It is used under license from LizardTech. MrSID is protected by U.S. Patent No. 5,710,835. Foreign patents pending. The U.S. Government and the University have reserved rights in MrSID technology, including without limitation: (a) The U.S. Government has a non-exclusive, nontransferable, irrevocable, paid-up license to practice or have practiced throughout the world, for or on behalf of the United States, inventions covered by U.S. Patent No. 5,710,835 and has other rights under 35 U.S.C. § 200-212 and applicable implementing regulations; (b) If LizardTech's rights in the MrSID Technology terminate during the term of this Agreement, you may continue to use the Software. Any provisions of this license which could reasonably be deemed to do so would then protect the University and/or the U.S. Government; and (c) The University has no obligation to furnish any know-how, technical assistance, or technical data to users of MrSID software and makes no warranty or representation as to the validity of U.S. Patent 5,710,835 nor that the MrSID Software will not infringe any patent or other proprietary right. For further information about these provisions, contact LizardTech, 1008 Western Ave., Suite 200, Seattle, WA 98104.

ERDAS, ERDAS IMAGINE, Stereo Analyst, IMAGINE Essentials, IMAGINE Advantage, IMAGINE, Professional, IMAGINE VirtualGIS, Mapcomposer, Viewfinder, and Imagizer are registered trademarks of ERDAS, Inc.

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List of Figures

Figure 1: Pixels and Bands in a Raster Image . . . 2

Figure 2: Typical File Coordinates . . . 4

Figure 3: Electromagnetic Spectrum . . . 5

Figure 4: Sun Illumination Spectral Irradiance at the Earth’s Surface . . . 7

Figure 5: Factors Affecting Radiation . . . 8

Figure 6: Reflectance Spectra . . . 10

Figure 7: Laboratory Spectra of Clay Minerals in the Infrared Region . . . 12

Figure 8: IFOV . . . 16

Figure 9: Brightness Values . . . 17

Figure 10: Landsat TM—Band 2 (Four Types of Resolution) . . . 17

Figure 11: Band Interleaved by Line (BIL) . . . 20

Figure 12: Band Sequential (BSQ) . . . 21

Figure 13: Image Files Store Raster Layers . . . 26

Figure 14: Example of a Thematic Raster Layer . . . 27

Figure 15: Examples of Continuous Raster Layers . . . 28

Figure 16: Vector Elements . . . 41

Figure 17: Vertices . . . 42

Figure 18: Workspace Structure . . . 45

Figure 19: Attribute CellArray . . . 46

Figure 20: Symbolization Example . . . 48

Figure 21: Digitizing Tablet . . . 49

Figure 22: Raster Format Converted to Vector Format . . . 52

Figure 23: Multispectral Imagery Comparison . . . 68

Figure 24: Landsat MSS vs. Landsat TM . . . 79

Figure 25: SPOT Panchromatic vs. SPOT XS . . . 89

Figure 26: SLAR Radar . . . 94

Figure 27: Received Radar Signal . . . 94

Figure 28: Radar Reflection from Different Sources and Distances . . . 95

Figure 29: ADRG Overview File Displayed in a Viewer . . . 112

Figure 30: Subset Area with Overlapping ZDRs . . . 113

Figure 31: Seamless Nine Image DR . . . 117

Figure 32: ADRI Overview File Displayed in a Viewer. . . 118

Figure 33: Arc/second Format . . . 122

Figure 34: Common Uses of GPS Data . . . 127

Figure 35: Example of One Seat with One Display and Two Screens . . . 145

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Figure 37: Transforming Data File Values to a Colorcell Value . . . 151

Figure 38: Transforming Data File Values to Screen Values . . . 152

Figure 39: Contrast Stretch and Colorcell Values . . . 155

Figure 40: Stretching by Min/Max vs. Standard Deviation . . . 156

Figure 41: Continuous Raster Layer Display Process . . . 157

Figure 42: Thematic Raster Layer Display Process . . . 160

Figure 43: Pyramid Layers . . . 165

Figure 44: Example of Dithering . . . 166

Figure 45: Example of Color Patches . . . 167

Figure 46: Data Input . . . 176

Figure 47: Raster Attributes for lnlandc.img . . . 181

Figure 48: Vector Attributes CellArray . . . 183

Figure 49: Proximity Analysis . . . 186

Figure 50: Contiguity Analysis . . . 187

Figure 51: Using a Mask . . . 188

Figure 52: Sum Option of Neighborhood Analysis (Image Interpreter) . . . 190

Figure 53: Overlay . . . 192

Figure 54: Indexing . . . 193

Figure 55: Graphical Model for Sensitivity Analysis . . . 196

Figure 56: Graphical Model Structure . . . 197

Figure 57: Modeling Objects . . . 200

Figure 58: Graphical and Script Models For Tasseled Cap Transformation . . . . 204

Figure 59: Layer Errors . . . 210

Figure 60: Sample Scale Bars . . . 217

Figure 61: Sample Legend . . . 220

Figure 62: Sample Neatline, Tick Marks, and Grid Lines. . . 221

Figure 63: Sample Symbols . . . 222

Figure 64: Sample Sans Serif and Serif Typefaces with Various Styles Applied . . 225

Figure 65: Good Lettering vs. Bad Lettering . . . 226

Figure 66: Projection Types . . . 229

Figure 67: Tangent and Secant Cones . . . 230

Figure 68: Tangent and Secant Cylinders . . . 231

Figure 69: Ellipse . . . 241

Figure 70: Polynomial Curve vs. GCPs . . . 259

Figure 71: Linear Transformations . . . 261

Figure 72: Nonlinear Transformations . . . 262

Figure 73: Transformation Example—1st-Order . . . 265

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Figure 75: Transformation Example—2nd-Order . . . 266

Figure 76: Transformation Example—4th GCP Added . . . 266

Figure 77: Transformation Example—3rd-Order . . . 267

Figure 78: Transformation Example—Effect of a 3rd-Order Transformation . . . . 267

Figure 79: Triangle Network . . . 269

Figure 80: Residuals and RMS Error Per Point . . . 272

Figure 81: RMS Error Tolerance . . . 273

Figure 82: Resampling . . . 275

Figure 83: Nearest Neighbor . . . 276

Figure 84: Bilinear Interpolation . . . 278

Figure 85: Linear Interpolation . . . 278

Figure 86: Cubic Convolution . . . 282

Figure 87: Layout for a Book Map and a Paneled Map . . . 290

Figure 88: Sample Map Composition . . . 292

Figure 89: Albers Conical Equal Area Projection . . . 305

Figure 90: Polar Aspect of the Azimuthal Equidistant Projection . . . 308

Figure 91: Behrmann Cylindrical Equal-Area Projection . . . 310

Figure 92: Bonne Projection . . . 312

Figure 93: Cassini Projection . . . 314

Figure 94: Cylindrical Equal-Area Projection . . . 316

Figure 95: Eckert I Projection . . . 320

Figure 96: Eckert II Projection . . . 322

Figure 97: Eckert III Projection . . . 324

Figure 98: Eckert IV Projection . . . 326

Figure 99: Eckert V Projection . . . 328

Figure 100: Eckert VI Projection . . . 330

Figure 101: Equidistant Conic Projection . . . 334

Figure 102: Equirectangular Projection . . . 337

Figure 103: Geographic Projection . . . 343

Figure 104: Hammer Projection . . . 347

Figure 105: Interrupted Goode Homolosine Projection . . . 349

Figure 106: Interrupted Mollweide Projection . . . 350

Figure 107: Lambert Azimuthal Equal Area Projection . . . 355

Figure 108: Lambert Conformal Conic Projection . . . 358

Figure 109: Loximuthal Projection . . . 362

Figure 110: Mercator Projection . . . 365

Figure 111: Miller Cylindrical Projection. . . 367

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Figure 113: Mollweide Projection. . . 373

Figure 114: Oblique Mercator Projection . . . 378

Figure 115: Orthographic Projection . . . 381

Figure 116: Plate Carrée Projection. . . 382

Figure 117: Polar Stereographic Projection and its Geometric Construction . . . . 385

Figure 118: Polyconic Projection of North America . . . 387

Figure 119: Quartic Authalic Projection . . . 389

Figure 120: Robinson Projection . . . 391

Figure 121: Sinusoidal Projection . . . 394

Figure 122: Space Oblique Mercator Projection . . . 396

Figure 123: Zones of the State Plane Coordinate System . . . 399

Figure 124: Stereographic Projection . . . 410

Figure 125: Two Point Equidistant Projection . . . 415

Figure 126: Zones of the Universal Transverse Mercator Grid in the United States . .

417 Figure 127: Van der Grinten I Projection . . . 420

Figure 128: Wagner IV Projection . . . 422

Figure 129: Wagner VII Projection . . . 424

Figure 130: Winkel I Projection . . . 426

Figure 131: Winkel’s Tripel Projection . . . 442

Figure 132: Histograms of Radiometrically Enhanced Data . . . 463

Figure 133: Graph of a Lookup Table . . . 464

Figure 134: Enhancement with Lookup Tables . . . 465

Figure 135: Nonlinear Radiometric Enhancement . . . 466

Figure 136: Piecewise Linear Contrast Stretch . . . 467

Figure 137: Contrast Stretch Using Lookup Tables, and Effect on Histogram . . . 469

Figure 138: Histogram Equalization . . . 470

Figure 139: Histogram Equalization Example . . . 471

Figure 140: Equalized Histogram. . . 472

Figure 141: Histogram Matching . . . 473

Figure 142: Spatial Frequencies . . . 475

Figure 143: Applying a Convolution Kernel . . . 476

Figure 144: Output Values for Convolution Kernel . . . 477

Figure 145: Local Luminance Intercept . . . 483

Figure 146: Schematic Diagram of the Discrete Wavelet Transform - DWT . . . 486

Figure 147: Inverse Discrete Wavelet Transform - DWT-1 . . . 487

Figure 148: Wavelet Resolution Merge . . . 488

Figure 149: Two Band Scatterplot . . . 492

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Figure 150: First Principal Component . . . 493

Figure 151: Range of First Principal Component . . . 493

Figure 152: Second Principal Component . . . 494

Figure 153: Intensity, Hue, and Saturation Color Coordinate System . . . 499

Figure 154: One-Dimensional Fourier Analysis . . . 512

Figure 155: Example of Fourier Magnitude . . . 514

Figure 156: The Padding Technique . . . 516

Figure 157: Comparison of Direct and Fourier Domain Processing . . . 518

Figure 158: An Ideal Cross Section . . . 520

Figure 159: High-Pass Filtering Using the Ideal Window. . . 521

Figure 160: Filtering Using the Bartlett Window . . . 521

Figure 161: Filtering Using the Butterworth Window . . . 522

Figure 162: Homomorphic Filtering Process . . . 524

Figure 163: Effects of Mean and Median Filters . . . 527

Figure 164: Regions of Local Region Filter . . . 528

Figure 165: One-dimensional, Continuous Edge, and Line Models . . . 533

Figure 166: A Noisy Edge Superimposed on an Ideal Edge . . . 533

Figure 167: Edge and Line Derivatives . . . 534

Figure 168: Adjust Brightness Function . . . 540

Figure 169: Range Lines vs. Lines of Constant Range . . . 541

Figure 170: Example of a Feature Space Image . . . 554

Figure 171: Process for Defining a Feature Space Object . . . 556

Figure 172: ISODATA Arbitrary Clusters . . . 559

Figure 173: ISODATA First Pass . . . 560

Figure 174: ISODATA Second Pass . . . 560

Figure 175: RGB Clustering . . . 563

Figure 176: Ellipse Evaluation of Signatures . . . 568

Figure 177: Classification Flow Diagram . . . 575

Figure 178: Parallelepiped Classification With Two Standard Deviations as Limits 576

Figure 179: Parallelepiped Corners Compared to the Signature Ellipse . . . 578

Figure 180: Feature Space Classification . . . 578

Figure 181: Minimum Spectral Distance . . . 580

Figure 182: Knowledge Engineer Editing Window . . . 586

Figure 183: Example of a Decision Tree Branch . . . 587

Figure 184: Split Rule Decision Tree Branch . . . 587

Figure 185: Knowledge Classifier Classes of Interest . . . 588

Figure 186: Histogram of a Distance Image . . . 590

Figure 187: Interactive Thresholding Tips . . . 591

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Figure 188: Exposure Stations Along a Flight Path . . . 600

Figure 189: A Regular Rectangular Block of Aerial Photos . . . 601

Figure 190: Pixel Coordinates and Image Coordinates . . . 604

Figure 191: Image Space and Ground Space Coordinate System . . . 605

Figure 192: Terrestrial Photography . . . 606

Figure 193: Internal Geometry. . . 607

Figure 194: Pixel Coordinate System vs. Image Space Coordinate System . . . 609

Figure 195: Radial vs. Tangential Lens Distortion . . . 610

Figure 196: Elements of Exterior Orientation . . . 612

Figure 197: Space Forward Intersection . . . 616

Figure 198: Photogrammetric Configuration . . . 617

Figure 199: GCP Configuration . . . 625

Figure 200: GCPs in a Block of Images . . . 625

Figure 201: Point Distribution for Triangulation . . . 626

Figure 202: Tie Points in a Block . . . 627

Figure 203: Image Pyramid for Matching at Coarse to Full Resolution . . . 632

Figure 204: Perspective Centers of SPOT Scan Lines . . . 634

Figure 205: Image Coordinates in a Satellite Scene . . . 635

Figure 206: Interior Orientation of a SPOT Scene . . . 636

Figure 207: Inclination of a Satellite Stereo-Scene (View from North to South) . . 638

Figure 208: Velocity Vector and Orientation Angle of a Single Scene . . . 639

Figure 209: Ideal Point Distribution Over a Satellite Scene for Triangulation . . . . 641

Figure 210: Orthorectification . . . 642

Figure 211: Digital Orthophoto—Finding Gray Values . . . 642

Figure 212: Regularly Spaced Terrain Data Points . . . 646

Figure 213: 3 × 3 Window Calculates the Slope at Each Pixel . . . 648

Figure 214: Slope Calculation Example . . . 649

Figure 215: 3 × 3 Window Calculates the Aspect at Each Pixel . . . 650

Figure 216: Aspect Calculation Example . . . 651

Figure 217: Shaded Relief . . . 652

Figure 218: Doppler Cone . . . 664

Figure 219: Sparse Mapping and Output Grids . . . 665

Figure 220: Magnitude and Phase Data as shown in the complex plane . . . 666

Figure 221: IMAGINE StereoSAR DEM Process Flow . . . 668

Figure 222: SAR Image Intersection . . . 669

Figure 223: UL Corner of the Reference Image . . . 674

Figure 224: UL Corner of the Match Image . . . 674

Figure 225: Image Pyramid . . . 675

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Figure 226: Electromagnetic Wave . . . 680

Figure 227: Variation of Electric Field in Time . . . 681

Figure 228: Effect of Time and Distance on Energy . . . 682

Figure 229: Geometric Model for an Interferometric SAR System . . . 683

Figure 230: Differential Collection Geometry . . . 686

Figure 231: Interferometric Phase Image without Filtering . . . 691

Figure 232: Interferometric Phase Image with Filtering . . . 691

Figure 233: Interferometric Phase Image without Phase Flattening . . . 692

Figure 234: Electromagnetic Wave Traveling through Space . . . 693

Figure 235: One-dimensional Continuous vs. Wrapped Phase Function . . . 694

Figure 236: Sequence of Unwrapped Phase Images . . . 695

Figure 237: Wrapped vs. Unwrapped Phase Images . . . 696

Figure 238: Histogram . . . 698

Figure 239: Normal Distribution . . . 701

Figure 240: Measurement Vector . . . 706

Figure 241: Mean Vector . . . 707

Figure 242: Two Band Plot . . . 708

Figure 243: Two-band Scatterplot . . . 709

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List of Tables

Description of File Types 44

Raster Data Formats 56

Annotation Data Formats 63

Vector Data Formats 64

AVNIR-2 Sensor Characteristics 69

PRISM Sensor Characteristics 70

ASTER Characteristics 70

EROS A - EROS B Characteristics 71

FORMOSAT-2 Characteristics 72

GeoEye-1 Characteristics 72

KOMPSAT-1 and KOMPSAT-2 Characteristics 75

AVHRR Data Characteristics 83

QuickBird Characteristics 85

RapidEye Characteristics 86

WorldView-1 Characteristics 91

WorldView-2 Characteristics 92

Commonly Used Bands for Radar Imaging 95

PALSAR Sensor Characteristics 98

COSMO-SkyMed Imaging Characteristics 98

JERS-1 Bands and Wavelengths 102

RADARSAT Beam Mode Resolution 103

RADARSAT-2 Characteristics 104

SIR-C/X-SAR Bands and Frequencies 106

TerraSAR-X Imaging Characteristics 106

Daedalus TMS Bands and Wavelengths 108

ARC System Chart Types 114

Legend Files for the ARC System Chart Types 115

Common Raster Data Products 127

File Types Created by Screendump 136

Common TIFF Format Elements 136

Conversion of DXF Entries 140

Conversion of IGES Entities 142

Colorcell Example 148

Commonly Used RGB Colors 159

Example of a Recoded Land Cover Layer 190

Model Maker Functions 198

General Editing Operations and Supporting Feature Types 207

Comparison of Building and Cleaning Coverages 208

Common Map Scales 217

Pixels per Inch 218

Acres and Hectares per Pixel 219

Map Projections 237

Projection Parameters 238

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Non-Earth Spheroids for use with ERDAS IMAGINE 246

NAD27 State Plane Coordinate System for the United States 399

NAD83 State Plane Coordinate System for the United States 404

UTM Zones, Central Meridians, and Longitude Ranges 417

Description of Modeling Functions Available for Enhancement 457

Theoretical Coefficient of Variation Values 529

Training Sample Comparison 553

Scanning Resolutions 602

SAR Parameters Required for Georeferencing 657

STD_LP_HD Correlator 675

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Preface

Introduction

The purpose of the ERDAS Field Guide™ is to provide background information on why one might use particular geographic information system (GIS) and image processing functions and how the software is manipulating the data, rather than what buttons to push to actually perform those functions. This book is also aimed at a diverse audience: from those who are new to geoprocessing to those savvy users who have been in this industry for years. For the novice, the ERDAS Field

Guide provides a brief history of the field, an extensive glossary of

terms, and notes about applications for the different processes

described. For the experienced user, the ERDAS Field Guide includes the formulas and algorithms that are used in the code, so that he or she can see exactly how each operation works.

Although the ERDAS Field Guide is primarily a reference to basic image processing and GIS concepts, it is geared toward ERDAS IMAGINE® users and the functions within ERDAS IMAGINE software, such as GIS analysis, image processing, cartography and map projections, graphics display hardware, statistics, and remote sensing. However, in some cases, processes and functions are described that may not be in the current version of the software, but planned for a future release. There may also be functions described that are not available on your system, due to the actual package that you are using.

The enthusiasm with which the first four editions of the ERDAS Field

Guide were received has been extremely gratifying, both to the authors

and to Leica Geosystems GIS & Mapping, LLC as a whole. First conceived as a helpful manual for users, the ERDAS Field Guide is now being used as a textbook, lab manual, and training guide throughout the world.

The ERDAS Field Guide will continue to expand and improve to keep pace with the profession. Suggestions and ideas for future editions are always welcome, and should be addressed to the Technical Writing department of Engineering at Leica Geosystems, in Norcross, Georgia.

Conventions Used

in this Book

The following paragraphs are used throughout the ERDAS Field Guide and other ERDAS IMAGINE documentation.

These paragraphs contain strong warnings or important tips.

These paragraphs lead you to other chapters in the ERDAS Field Guide or other manuals for additional information.

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These paragraphs give you additional information.

These paragraphs provide software version specific information. NOTE: Notes give additional instruction

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Raster Data

Introduction

The ERDAS IMAGINE system incorporates the functions of both image

processing and GIS. These functions include importing, viewing, altering, and analyzing raster and vector data sets.

This chapter is an introduction to raster data, including: • remote sensing

• data storage formats • different types of resolution • radiometric correction • geocoded data • raster data in GIS

See "Vector Data" on page 41 for more information on vector data.

Image Data

In general terms, an image is a digital picture or representation of an object. Remotely sensed image data are digital representations of the Earth. Image data are stored in data files, also called image files, on magnetic tapes, computer disks, or other media. The data consist only of numbers. These representations form images when they are displayed on a screen or are output to hardcopy.

Each number in an image file is a data file value. Data file values are sometimes referred to as pixels. The term pixel is abbreviated from picture element. A pixel is the smallest part of a picture (the area being scanned) with a single value. The data file value is the measured brightness value of the pixel at a specific wavelength.

Raster image data are laid out in a grid similar to the squares on a checkerboard. Each cell of the grid is represented by a pixel, also known as a grid cell.

In remotely sensed image data, each pixel represents an area of the Earth at a specific location. The data file value assigned to that pixel is the record of reflected radiation or emitted heat from the Earth’s surface at that location.

Data file values may also represent elevation, as in digital elevation models (DEMs).

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The terms pixel and data file value are not interchangeable in ERDAS IMAGINE. Pixel is used as a broad term with many meanings, one of which is data file value. One pixel in a file may consist of many data file values. When an image is displayed or printed, other types of values are represented by a pixel.

See "Image Display" on page 145 for more information on how images are displayed.

Bands

Image data may include several bands of information. Each band is a set of data file values for a specific portion of the electromagnetic spectrum of reflected light or emitted heat (red, green, blue, near-infrared, near-infrared, thermal, and so forth) or some other user-defined information created by combining or enhancing the original bands, or creating new bands from other sources.

ERDAS IMAGINE programs can handle an unlimited number of bands of image data in a single file.

Figure 1: Pixels and Bands in a Raster Image

See "Enhancement" on page 455 for more information on combining or enhancing bands of data.

Bands vs. Layers

In ERDAS IMAGINE, bands of data are occasionally referred to as layers. Once a band is imported into a GIS, it becomes a layer of information which can be processed in various ways. Additional layers can be created and added to the image file (.img extension) in ERDAS IMAGINE, such as layers created by combining existing layers.

Read more about image files in ERDAS IMAGINE Format (.img)

on page 25.

1 pixel

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Layers vs. Viewer Layers

The Viewer permits several images to be layered, in which case each image (including a multiband image) may be a layer.

Numeral Types

The range and the type of numbers used in a raster layer determine how the layer is displayed and processed. For example, a layer of elevation data with values ranging from -51.257 to 553.401 would be treated differently from a layer using only two values to show land and water.

The data file values in raster layers generally fall into these categories: • Nominal data file values are simply categorized and named. The

actual value used for each category has no inherent meaning—it is simply a class value. An example of a nominal raster layer would be a thematic layer showing tree species.

• Ordinal data are similar to nominal data, except that the file values put the classes in a rank or order. For example, a layer with classes numbered and named:

1 - Good, 2 - Moderate, and 3 - Poor is an ordinal system.

• Interval data file values have an order, but the intervals between the values are also meaningful. Interval data measure some

characteristic, such as elevation or degrees Fahrenheit, which does not necessarily have an absolute zero. (The difference between two values in interval data is meaningful.)

• Ratio data measure a condition that has a natural zero, such as electromagnetic radiation (as in most remotely sensed data), rainfall, or slope.

Nominal and ordinal data lend themselves to applications in which categories, or themes, are used. Therefore, these layers are sometimes called categorical or thematic.

Likewise, interval and ratio layers are more likely to measure a condition, causing the file values to represent continuous gradations across the layer. Such layers are called continuous.

Coordinate Systems

The location of a pixel in a file or on a displayed or printed image is expressed using a coordinate system. In two-dimensional coordinate systems, locations are organized in a grid of columns and rows. Each location on the grid is expressed as a pair of coordinates known as X and Y. The X coordinate specifies the column of the grid, and the Y coordinate specifies the row. Image data organized into such a grid are known as raster data.

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• file coordinates—indicate the location of a pixel within the image (data file)

• map coordinates—indicate the location of a pixel in a map

File Coordinates

File coordinates refer to the location of the pixels within the image (data) file. File coordinates for the pixel in the upper left corner of the image always begin at 0, 0.

Figure 2: Typical File Coordinates

Map Coordinates

Map coordinates may be expressed in one of a number of map coordinate or projection systems. The type of map coordinates used by a data file depends on the method used to create the file (remote sensing, scanning an existing map, and so forth). In ERDAS IMAGINE, a data file can be converted from one map coordinate system to another.

For more information on map coordinates and projection systems, see "Cartography" on page 211 or "Map Projections" on page 297. See "Rectification" on page 251 for more information on changing the map coordinate system of a data file.

Remote Sensing

Remote sensing is the acquisition of data about an object or scene by a sensor that is far from the object (Colwell, 1983). Aerial photography, satellite imagery, and radar are all forms of remotely sensed data. Usually, remotely sensed data refer to data of the Earth collected from sensors on satellites or aircraft. Most of the images used as input to the ERDAS IMAGINE system are remotely sensed. However, you are not limited to remotely sensed data.

rows (y) (3,1)_x,y

columns (x) 0 1 2 3 0 1 2 3 4

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This section is a brief introduction to remote sensing. There are many books available for more detailed information, including

Colwell, 1983, Swain and Davis, 1978; and Slater, 1980 (see

"Bibliography" on page 777).

Electromagnetic Radiation Spectrum

The sensors on remote sensing platforms usually record

electromagnetic radiation. Electromagnetic radiation (EMR) is energy transmitted through space in the form of electric and magnetic waves (Star and Estes, 1990). Remote sensors are made up of detectors that record specific wavelengths of the electromagnetic spectrum. The electromagnetic spectrum is the range of electromagnetic radiation extending from cosmic waves to radio waves ("Jensen, 1996"). All types of land cover (rock types, water bodies, and so forth) absorb a portion of the electromagnetic spectrum, giving a distinguishable signature of electromagnetic radiation. Armed with the knowledge of which wavelengths are absorbed by certain features and the intensity of the reflectance, you can analyze a remotely sensed image and make fairly accurate assumptions about the scene. Figure 3: illustrates the electromagnetic spectrum (Suits, 1983; Star and Estes, 1990).

Figure 3: Electromagnetic Spectrum

SWIR and LWIR

The near-infrared and middle-infrared regions of the electromagnetic spectrum are sometimes referred to as the short wave infrared region (SWIR). This is to distinguish this area from the thermal or far infrared region, which is often referred to as the long wave infrared region (LWIR). The SWIR is characterized by reflected radiation whereas the LWIR is characterized by emitted radiation.

micrometers μm (one millionth of a meter)

0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 Reflected Thermal SWIR LWIR Visible (0.4 - 0.7) Blue (0.4 - 0.5) Green (0.5 - 0.6) Red (0.6 - 0.7) Near-infrared (0.7 - 2.0) Ultraviolet Middle-infrared (2.0 - 5.0) Far-infrared(8.0 - 15.0) Radar

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Absorption / Reflection

Spectra

When radiation interacts with matter, some wavelengths are absorbed and others are reflected.To enhance features in image data, it is necessary to understand how vegetation, soils, water, and other land covers reflect and absorb radiation. The study of the absorption and reflection of EMR waves is called spectroscopy.

Spectroscopy

Most commercial sensors, with the exception of imaging radar sensors, are passive solar imaging sensors. Passive solar imaging sensors can only receive radiation waves; they cannot transmit radiation. (Imaging radar sensors are active sensors that emit a burst of microwave radiation and receive the backscattered radiation.)

The use of passive solar imaging sensors to characterize or identify a material of interest is based on the principles of spectroscopy.

Therefore, to fully utilize a visible/infrared (VIS/IR) multispectral data set and properly apply enhancement algorithms, it is necessary to

understand these basic principles. Spectroscopy reveals the:

• absorption spectra—the EMR wavelengths that are absorbed by specific materials of interest

• reflection spectra—the EMR wavelengths that are reflected by specific materials of interest

Absorption Spectra

Absorption is based on the molecular bonds in the (surface) material. Which wavelengths are absorbed depends upon the chemical

composition and crystalline structure of the material. For pure compounds, these absorption bands are so specific that the SWIR region is often called an infrared fingerprint.

Atmospheric Absorption

In remote sensing, the sun is the radiation source for passive sensors. However, the sun does not emit the same amount of radiation at all wavelengths. Figure 4 shows the solar irradiation curve, which is far from linear.

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Figure 4: Sun Illumination Spectral Irradiance at the Earth’s Surface

Source: Modified from Chahine et al, 1983

Solar radiation must travel through the Earth’s atmosphere before it reaches the Earth’s surface. As it travels through the atmosphere, radiation is affected by four phenomena (Elachi, 1987):

• absorption—the amount of radiation absorbed by the atmosphere • scattering—the amount of radiation scattered away from the field of

view by the atmosphere

• scattering source—divergent solar irradiation scattered into the field of view

• emission source—radiation re-emitted after absorption

0 0.0 Eλ Sp ectral Irr a di ance ( W m -2 m -1 ) 3.0 1.5 1.8 2.1 2.4 2.7 1.2 0.9 0.6 0.3 Wavelength μm 2500 2000 1500 1000 500 UV VIS INFRARED

Solar irradiation curve outside atmosphere

Solar irradiation curve at sea level

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Figure 5: Factors Affecting Radiation

Source: Elachi, 1987

Absorption is not a linear phenomena—it is logarithmic with concentration (Flaschka, 1969). In addition, the concentration of atmospheric gases, especially water vapor, is variable. The other major gases of importance are carbon dioxide (CO₂) and ozone (O₃), which can vary considerably around urban areas. Thus, the extent of

atmospheric absorbance varies with humidity, elevation, proximity to (or downwind of) urban smog, and other factors.

Scattering is modeled as Rayleigh scattering with a commonly used algorithm that accounts for the scattering of short wavelength energy by the gas molecules in the atmosphere (Pratt, 1991)—for example, ozone. Scattering is variable with both wavelength and atmospheric aerosols. Aerosols differ regionally (ocean vs. desert) and daily (for example, Los Angeles smog has different concentrations daily). Scattering source and emission source may account for only 5% of the variance. These factors are minor, but they must be considered for accurate calculation. After interaction with the target material, the reflected radiation must travel back through the atmosphere and be subjected to these phenomena a second time to arrive at the satellite.

Absorption—the amount of

Scattering—the amount of radiation

Scattering Source—divergent solar

Emission Source—radiation

Radiation

radiation absorbed by the atmosphere

re-emitted after absorption

scattered away from the field of view

irradiations scattered into the field of view

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The mathematical models that attempt to quantify the total atmospheric effect on the solar illumination are called radiative transfer equations. Some of the most commonly used are Lowtran (Kneizys et al, 1988) and Modtran (Berk et al, 1989).

See "Enhancement" on page 455 for more information on atmospheric modeling.

Reflectance Spectra

After rigorously defining the incident radiation (solar irradiation at target), it is possible to study the interaction of the radiation with the target material. When an electromagnetic wave (solar illumination in this case) strikes a target surface, three interactions are possible (Elachi, 1987):

• reflection • transmission • scattering

It is the reflected radiation, generally modeled as bidirectional

reflectance (Clark and Roush, 1984), that is measured by the remote sensor.

Remotely sensed data are made up of reflectance values. The resulting reflectance values translate into discrete digital numbers (or values) recorded by the sensing device. These gray scale values fit within a certain bit range (such as 0 to 255, which is 8-bit data) depending on the characteristics of the sensor.

Each satellite sensor detector is designed to record a specific portion of the electromagnetic spectrum. For example, Landsat Thematic Mapper (TM) band 1 records the 0.45 to 0.52 μm portion of the spectrum and is designed for water body penetration, making it useful for coastal water mapping. It is also useful for soil/vegetation discriminations, forest type mapping, and cultural features identification (Lillesand and Kiefer, 1987).

The characteristics of each sensor provide the first level of constraints on how to approach the task of enhancing specific features, such as vegetation or urban areas. Therefore, when choosing an enhancement technique, one should pay close attention to the characteristics of the land cover types within the constraints imposed by the individual sensors.

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The use of VIS/IR imagery for target discrimination, whether the target is mineral, vegetation, man-made, or even the atmosphere itself, is based on the reflectance spectrum of the material of interest (see

Figure 6). Every material has a characteristic spectrum based on the chemical composition of the material. When sunlight (the illumination source for VIS/IR imagery) strikes a target, certain wavelengths are absorbed by the chemical bonds; the rest are reflected back to the sensor. It is, in fact, the wavelengths that are not returned to the sensor that provide information about the imaged area.

Specific wavelengths are also absorbed by gases in the atmosphere (H₂O vapor, CO₂, O₂, and so forth). If the atmosphere absorbs a large percentage of the radiation, it becomes difficult or impossible to use that particular wavelength(s) to study the Earth. For the present Landsat and Systeme Pour l’observation de la Terre (SPOT) sensors, only the water vapor bands are considered strong enough to exclude the use of their spectral absorption region. Figure 6 shows how Landsat TM bands 5 and 7 were carefully placed to avoid these regions. Absorption by other atmospheric gases was not extensive enough to eliminate the use of the spectral region for present day broad band sensors.

Figure 6: Reflectance Spectra

100 80 60 40 20 0 Refle c ta nce , % .4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength, μm Vegetation (green) Silt loam Atmospheric bands 4 5 6 7 _{Landsat MSS bands} 1 2 3 4 5 7 Landsat TM bands Kaolinite absorption

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Source: Modified from Fraser, 1986;Crist et al, 1986; Sabins, 1987

NOTE: This chart is for comparison purposes only. It is not meant to show actual values. The spectra are offset to better display the lines.

An inspection of the spectra reveals the theoretical basis of some of the indices in the ERDAS IMAGINE Image Interpreter. Consider the vegetation index TM4/TM3. It is readily apparent that for vegetation this value could be very large. For soils, the value could be much smaller, and for clay minerals, the value could be near zero. Conversely, when the clay ratio TM5/TM7 is considered, the opposite applies.

Hyperspectral Data

As remote sensing moves toward the use of more and narrower bands (for example, AVIRIS with 224 bands each only 10 nm wide),

absorption by specific atmospheric gases must be considered. These multiband sensors are called hyperspectral sensors. As more and more of the incident radiation is absorbed by the atmosphere, the digital number (DN) values of that band get lower, eventually becoming useless—unless one is studying the atmosphere. Someone wanting to measure the atmospheric content of a specific gas could utilize the bands of specific absorption.

NOTE: Hyperspectral bands are generally measured in nanometers (nm).

Figure 6 shows the spectral bandwidths of the channels for the Landsat sensors plotted above the absorption spectra of some common natural materials (kaolin clay, silty loam soil, and green vegetation). Note that while the spectra are continuous, the Landsat channels are segmented or discontinuous. We can still use the spectra in interpreting the Landsat data. For example, a Normalized Difference Vegetation Index (NDVI) ratio for the three would be very different and, therefore, could be used to discriminate between the three materials. Similarly, the ratio TM5/TM7 is commonly used to measure the concentration of clay minerals. Evaluation of the spectra shows why.

Figure 7 shows detail of the absorption spectra of three clay minerals. Because of the wide bandpass (2080 to 2350 nm) of TM band 7, it is not possible to discern between these three minerals with the Landsat sensor. As mentioned, the AVIRIS hyperspectral sensor has a large number of approximately 10 nm wide bands. With the proper selection of band ratios, mineral identification becomes possible. With this data set, it would be possible to discriminate between these three clay minerals, again using band ratios. For example, a color composite image prepared from RGB = 2160nm/2190nm, 2220nm/2250nm, 2350nm/2488nm could produce a color-coded clay mineral image-map.

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The commercial airborne multispectral scanners are used in a similar fashion. The Airborne Imaging Spectrometer from the Geophysical & Environmental Research Corp. (GER) has 79 bands in the UV, visible, SWIR, and thermal-infrared regions. The Airborne Multispectral Scanner Mk2 by Geoscan Pty, Ltd., has up to 52 bands in the visible, SWIR, and thermal-infrared regions. To properly utilize these

hyperspectral sensors, you must understand the phenomenon involved and have some idea of the target materials being sought.

Figure 7: Laboratory Spectra of Clay Minerals in the Infrared Region

Source: Modified from Sabins, 1987

NOTE: Spectra are offset vertically for clarity.

2000 2200 2400 2600 Landsat TM band 7 2080 nm _{2350 nm} Kaolinite Montmorillonite Illite Re flec ta nce , % Wavelength, nm

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The characteristics of Landsat, AVIRIS, and other data types are discussed in "Raster and Vector Data Sources" on page 55. See

"Enhancement" on page 455 for more information on the NDVI ratio.

Imaging Radar Data

Radar remote sensors can be broken into two broad categories: passive and active. The passive sensors record the very low intensity, microwave radiation naturally emitted by the Earth. Because of the very low intensity, these images have low spatial resolution (that is, large pixel size).

It is the active sensors, termed imaging radar, that are introducing a new generation of satellite imagery to remote sensing. To produce an image, these satellites emit a directed beam of microwave energy at the target, and then collect the backscattered (reflected) radiation from the target scene. Because they must emit a powerful burst of energy, these satellites require large solar collectors and storage batteries. For this reason, they cannot operate continuously; some satellites are limited to 10 minutes of operation per hour.

The microwave energy emitted by an active radar sensor is coherent and defined by a narrow bandwidth. The following table summarizes the bandwidths used in remote sensing.

*Wavelengths commonly used in imaging radars are shown in parentheses. Band Designation* Wavelength (λ), cm Frequency (υ), GHz

(109 cycles · sec-1) Ka (0.86 cm) 0.8 to 1.1 40.0 to 26.5 K 1.1 to 1.7 26.5 to 18.0 Ku 1.7 to 2.4 18.0 to 12.5 X (3.0 cm, 3.2 cm) 2.4 to 3.8 12.5 to 8.0 C 3.8 to 7.5 8.0 to 4.0 S 7.5 to 15.0 4.0 to 2.0 L (23.5 cm, 25.0 cm) 15.0 to 30.0 2.0 to 1.0 P 30.0 to 100.0 1.0 to 0.3

ERDAS Field Guide

ERDAS Field Guide™

Table of Contents

Table of Contents . . . .iii

List of Figures . . . xix

List of Tables . . . xxvii

Preface . . . xxix

Introduction . . . xxix

Conventions Used in this Book . . . xxix

Raster Data . . . 1

Introduction . . . 1

Image Data . . . 1

Bands . . . .2

Coordinate Systems . . . .3

Remote Sensing . . . 4

Absorption / Reflection Spectra . . . .6

Resolution . . . 14

Spectral Resolution . . . .15

Spatial Resolution . . . .15

Radiometric Resolution . . . .16

Temporal Resolution . . . .17

Data Correction . . . 18

Line Dropout . . . .18

Striping . . . .18

Data Storage . . . 18

Storage Formats . . . .19

Storage Media . . . .22

Calculating Disk Space . . . .25

ERDAS IMAGINE Format (.img) . . . .25

Image File Organization . . . 29

Consistent Naming Convention . . . .29

Keeping Track of Image Files . . . .30

Geocoded Data . . . 31

Using Image Data in GIS . . . 31

Subsetting and Mosaicking . . . .31

Multispectral Classification . . . .33

Editing Raster Data . . . 33

Editing Continuous (Athematic) Data . . . .34

Interpolation Techniques . . . .34

Image Compression . . . 36

Dynamic Range Run-Length Encoding (DR RLE) . . . .36

ECW Compression . . . .38

Vector Data . . . 41

Introduction . . . 41

Points . . . .42

Lines . . . .42

Polygons . . . .42

Vertex . . . .42

Coordinates . . . .42

Vector Layers . . . .43

Topology . . . .43

Vector Files . . . .43

Attribute Information . . . 45

Displaying Vector Data . . . 47

Color Schemes . . . .47

Symbolization . . . .47

Vector Data Sources . . . 48

Digitizing . . . 49

Tablet Digitizing . . . .49

Screen Digitizing . . . .51

Imported Vector Data . . . 51

Raster to Vector Conversion . . . 52

Other Vector Data Types . . . 52

Shapefile Vector Format . . . .52

SDE . . . .53

SDTS . . . .53

ArcGIS Integration . . . .54

Raster and Vector Data Sources . . . 55

Importing and Exporting . . . 55

Raster Data Sources . . . .62

Annotation Data . . . .63

Generic Binary Data . . . .63

Vector Data . . . .64

Optical Satellite Data . . . 66

Satellite System . . . .66

Satellite Characteristics . . . .67

ALOS . . . .68

ASTER . . . .70

EROS A and EROS B . . . .71

FORMOSAT-2 . . . .71