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El. knyga: Image Processing and Data Analysis with ERDAS IMAGINE(R)

  • Formatas: 350 pages
  • Išleidimo metai: 03-Oct-2018
  • Leidėjas: CRC Press
  • Kalba: eng
  • ISBN-13: 9781351980586
  • Formatas: 350 pages
  • Išleidimo metai: 03-Oct-2018
  • Leidėjas: CRC Press
  • Kalba: eng
  • ISBN-13: 9781351980586

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Remotely sensed data, in the form of digital images captured from spaceborne and airborne platforms, provide a rich analytical and observational source of information about the current status, as well as changes occurring in, on, and around the Earths surface. The data products, or simply images processed from these platforms, provide an additional advantage in that geographic areas or regions of interest can be revisited on a regular cycle. This revisit cycle allows geospatial analysts and natural resource managers to explore changing conditions over time.

Image Processing and Data Analysis with ERDAS IMAGINE® explains the principles behind the processing of remotely sensed data in a simple, easy to understand, and "how-to" format. Organized as a step-by-step guide with exercises adapted from original research and using publicly available imagery, such as NASA Landsat, ESA Sentinel-2, Orthophotos, and others, this book gives readers the ability to quickly gain the practical experience needed to navigate the ERDAS IMAGINE® software as well as learn certain applications in Esris ArcMap ArcGIS for Desktop software and Quantum the GIS (QGIS) open source applications package. It also helps readers to easily move beyond the information presented in this book and tackle more advanced skills.

Written by two professors with long experience in remote sensing and image processing, this book is a useful guide and reference for both undergraduate and graduate students, researchers, instructors, managers, and agency professionals who are involved in the study of Earth systems and the environment.
Acknowledgments xi
Authors xiii
Introduction and Overview xv
1 Acquiring Data: EarthExplorer, GloVis, LandsatLook Viewer, and NRCS Geospatial Data Gateway 1(48)
Overview
1(3)
Acquiring Remotely Sensed Data
4(1)
Learning Objectives
5(42)
I Finding and Downloading Data in EarthExplorer
6(21)
II Automated Method of Importing EarthExplorer and Creating a Multi-Band, Layer Stack, Image in ERDAS IMAGINE
27(7)
III Finding and Downloading Data in GloVis
34(5)
IV Displaying Raster Data and Creating a Multi-Band Image in Esri ArcMap ArcGIS for Desktop
39(6)
V Displaying Raster Data and Creating a Multi-Band Image in Quantum Geographic Information Systems
45(2)
Review Questions
47(2)
2 Introduction to Image Data Processing 49(20)
Overview
49(3)
Introduction to Digital Image Processing Application
52(16)
Learning Objectives
52(1)
I Obtaining Required Data in EarthExplorer
53(3)
II ERDAS IMAGINE Graphic User Interface
56(15)
Exploring ERDAS Help Documents
57(2)
Setting Up Workspace Preferences
59(1)
Opening Images
60(2)
Getting Data Information
62(1)
Band Combinations
62(2)
Create a False Color Composite Display Band Combination
64(2)
Linking Images in Multiple 2D Viewer Windows
66(1)
Opening Multiple Images
66(2)
Review Questions
68(1)
3 Georectification 69(26)
Overview
69(1)
Image Preprocessing-Georectification
70(1)
Learning Objectives
70(1)
Rectifying Image of Schenk Forest Using Polynomial Regression and Rubber Sheeting
71(22)
I Polynomial Regression
71(15)
Display Images-Start Two Viewers (File|New|2D View)
71(3)
Record Ground Control Points
74(6)
Compute Transformation Matrix
80(1)
Resample the Image
81(1)
Verify Rectification
82(4)
II Rubber Sheeting
86(9)
Display Images
86(1)
Start Geometric Correction Tools
87(1)
Ground Control Points
87(2)
Compute Transformation Matrix
89(1)
Resample the Image
90(1)
Verify Rectification
91(2)
Review Questions
93(2)
4 Orthorectification 95(22)
Overview
95(1)
Image Preprocessing-Orthorectification
95(19)
Learning Objectives
96(1)
Getting Started-Orthorectification
96(3)
Defining Camera Properties (Interior Orientation)
99(10)
Selecting Ground Control Points
109(5)
Creating an Orthophoto
114(1)
Viewing the Orthophoto
115(1)
Review Questions
116(1)
5 Positional Accuracy Assessment 117(6)
Overview
117(2)
Positional Accuracy Application
119(1)
Learning Objectives
119(1)
Calculating Error in the X and Y Directions
119(1)
Root Mean Square Error
120(1)
Total Root Mean Square Error
120(1)
Euclidean Distance
121(1)
Review Questions
121(2)
6 Radiometric Image Enhancement 123(24)
Overview
123(1)
Radiometric Enhancement Application
124(1)
Learning Objectives
124(1)
Performing Radiometric Enhancements
125(8)
Loading Stretched and Non-Stretched Images
125(2)
Understanding the Stretch
127(2)
ERDAS IMAGINE and Lookup Tables (LUT Values)
129(4)
Adjusting the Stretch
133(4)
Making Finer Adjustments
137(9)
Review Questions
146(1)
7 Spatial Image Enhancement 147(6)
Overview
147(1)
Spatial Image Enhancement Application
148(1)
Learning Objectives
148(1)
Spatial Image Enhancements
148(3)
Initiate a 3x3 Filter
149(1)
Initiate a 5x5 High Pass Filter
150(1)
Initiate a 5x5 Low Pass Filter
150(1)
Open the Non-Direction Edge Filter Tool Dialog Window
150(1)
Open the Image Degrade Tool Dialog Window
151(1)
Review Questions
151(2)
8 Image Digitizing and Interpretation 153(14)
Overview
153(1)
Image Digitization and Interpretation Application
153(2)
Learning Objectives
154(1)
Polygon Creation
155(6)
Polyline Creation
161(3)
Manual Drawing
164(1)
Changing the Display Properties of the Digitizing Results
165(1)
Review Questions
166(1)
9 Unsupervised Classification 167(40)
Overview
167(39)
I Unsupervised Classification Application
169(21)
Learning Objectives
169(1)
Obtaining the Required Data (Review)
169(3)
Creating an Image Subset of the Cloud-Free Areas
172(4)
Initiating the Unsupervised Classification
176(1)
Unsupervised Classification Application Approach
177(5)
Creating a Reference Image Subset
182(2)
Assigning Class Categories to the Unsupervised Classification
184(4)
Compare
188(2)
II Unsupervised Classification in Esri ArcMap ArcGIS for Desktop
190(9)
III Unsupervised Classification in QGIS
199(7)
Review Questions
206(1)
10 Supervised Classification 207(24)
Overview
207(2)
Supervised Classification Application
209(1)
Learning Objectives
209(1)
Obtaining the Required Data (Review)
210(2)
Initiating the Supervised Classification
212(17)
Supervised Classification Application Approach-Creating a Signature File
213(5)
Tips for Creating the Supervised Classification
218(1)
Compare Using Swipe Tool
219(2)
Compare Using Image Difference Operation (Change Detection)
221(7)
Note on Image-To-Image Change Detection Comparisons
228(1)
Review Questions
229(2)
11 Object Based Image Analysis 231(18)
Overview
231(1)
Object Based Image Analysis Classification Application
232(13)
Learning Objectives
232(1)
I Feature Project Setup Procedure
233(4)
II Feature Extraction Procedure
237(8)
Examine the Object-Oriented Classification in ERDAS IMAGINE
245(1)
Review Questions
246(3)
12 Additional Image Analysis Techniques 249(24)
Overview
249(1)
Additional Analysis Techniques Application
250(21)
Learning Objectives
250(1)
I Create a Land-Only Image
251(7)
Initiating the Classification Procedure
252(1)
Create a Thematic Image Recode
253(3)
Create a Binary Image Mask to Remove Water
256(2)
II Create a Normalized Difference Vegetation Index
258(4)
Create a Normalized Difference Vegetation Index
258(2)
Classify the Normalized Difference Vegetation Index
260(2)
III Create an Impervious Surface Map
262(11)
Create an Impervious Surface Map (Remove Vegetation)
262(2)
Classify the Impervious Surface Map
264(2)
Recombine Classification Components
266(1)
Combining Output Layers in Model Maker
266(5)
Review Questions
271(2)
13 Assessing Thematic Classification Accuracy 273(16)
Overview
273(11)
I Assessing Thematic Classification Accuracy Application
274(1)
Learning Objectives
274(1)
II Recoding the Supervised Classification
275(2)
III The Accuracy Assessment Procedure
277(7)
IV Accuracy Assessment Report Generated from
ERDAS IMAGINE
284(3)
Review Questions
287(2)
14 Basics of Digital Stereoscopy 289(18)
Overview
289(1)
Basics of Digital Stereoscopy Application
289(1)
Learning Objectives
289(1)
Configuring the Stereo Analyst module
290(3)
Making a Digital Stereo Pair
293(5)
Creating Anaglyph Images for Export
298(2)
Delineating in Stereo
300(6)
Review Questions
306(1)
Appendix: Answer to
Chapter Review Questions
307(14)
References 321(2)
Index 323
Dr. Stacy A. C. Nelson is an associate professor in the College of Natural Resources Center for Geospatial Analytics, the Department of Forestry and Environmental Resources, and the Fisheries, Wildlife, and Conservation Science Program at North Carolina State University, since 2002. Dr. Nelsons research interests focus on the use of geospatial technologies to address both regional and localscale questions of land use and land cover change and the impact this change has on aquatic ecosystems. Dr. Nelson earned a B.S. in biology from Jackson State University (1990). He completed a masters degree from the College of William and Marys school of marine science-the Virginia Institute of Marine Science (1995). His Ph.D. was completed in Limnology from Michigan State Universitys Department of Fisheries and Wildlife (2002), where Dr. Nelson attended graduate school as a NASA graduate research fellow. During 2010, Dr. Nelson served a year-long Federal Intergovernmental Personnel Act appointment within the National Headquarters of the USDA Forest Services Office of Civil Rights in Washington D.C. In D.C., Dr. Nelson worked with the Forest Service and multiple agencies to expand working partnerships between majority-serving and minority-serving Land-Grant Universities in an effort to increase shared research capacities, curricula, and diversity among institutions.



Dr. Khorram is a Professor of Remote Sensing and Image processing. He holds a joint faculty appointment at UC Berkeley and North Carolina State University. He received a MS. in Engineering and another MS in Ecology from the University of California (UC) at Davis and his Ph.D. under a joint program from UC Berkeley and Davis. From 1976 to 1980, he served as the Principal Scientist at the Space Sciences Laboratory at UC Berkeley. He then joined the faculty in North Carolina State University (NCSU) in Forestry and Environmental Resources and in Electrical and Computer Engineering departments. He has served as the Principal Investigator for well over 60 major research projects. His research projects have focused on remote sensing, image processing, and geospatial information technology. He has developed a number of image classification and multiresolution data fusion systems as applied to land use and land cover classifications and geospatial modeling. The primary areas of interest and expertise in his research is in natural resources (including water quality) mapping, monitoring, and change detection based on conventional remote sensing techniques as well as automated procedures for image registration, classification, and change detection.