Image Analysis with ArcGIS. Student Edition
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1 Image Analysis with ArcGIS Student Edition
2 Copyright 2016 Esri All rights reserved. Course version 2.0. Version release date May Printed in the United States of America. The information contained in this document is the exclusive property of Esri. 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 Esri. All requests should be sent to Attention: Contracts and Legal Services Manager, Esri, 380 New York Street, Redlands, CA USA. EXPORT NOTICE: Use of these Materials is subject to U.S. export control laws and regulations including the U.S. Department of Commerce Export Administration Regulations (EAR). Diversion of these Materials contrary to U.S. law is prohibited. The information contained in this document is subject to change without notice. US Government Restricted/Limited Rights Any software, documentation, and/or data delivered hereunder is subject to the terms of the License Agreement. The commercial license rights in the License Agreement strictly govern Licensee's use, reproduction, or disclosure of the software, data, and documentation. In no event shall the US Government acquire greater than RESTRICTED/ LIMITED RIGHTS. At a minimum, use, duplication, or disclosure by the US Government is subject to restrictions as set forth in FAR Alternates I, II, and III (DEC 2007); FAR (b) (DEC 2007) and/or FAR / (Commercial Technical Data/Computer Software); and DFARS (DEC 2011) (Technical Data - Commercial Items) and/or DFARS (Commercial Computer Software and Commercial Computer Software Documentation), as applicable. Contractor/Manufacturer is Esri, 380 New York Street, Redlands, CA , 3D Analyst, ACORN, Address Coder, ADF, AML, ArcAtlas, ArcCAD, ArcCatalog, ArcCOGO, ArcData, ArcDoc, ArcEdit, ArcEditor, ArcEurope, ArcExplorer, ArcExpress, ArcGIS, ArcGlobe, ArcGrid, ArcIMS, ARC/INFO, ArcInfo, ArcInfo Librarian, ArcLessons, ArcLocation, ArcLogistics, ArcMap, ArcNetwork, ArcNews, ArcObjects, ArcOpen, ArcPad, ArcPlot, ArcPress, ArcPy, ArcReader, ArcScan, ArcScene, ArcSchool, ArcScripts, ArcSDE, ArcSdl, ArcSketch, ArcStorm, ArcSurvey, ArcTIN, ArcToolbox, ArcTools, ArcUSA, ArcUser, ArcView, ArcVoyager, ArcWatch, ArcWeb, ArcWorld, ArcXML, Atlas GIS, AtlasWare, Avenue, BAO, Business Analyst, Business Analyst Online, BusinessMAP, CityEngine, CommunityInfo, Database Integrator, DBI Kit, EDN, Esri, Esri Team GIS, Esri The GIS Company, Esri The GIS People, Esri The GIS Software Leader, FormEdit, GeoCollector, Geographic Design System, Geography Matters, Geography Network, GIS by Esri, GIS Day, GIS for Everyone, GISData Server, JTX, MapIt, Maplex, MapObjects, MapStudio, ModelBuilder, MOLE, MPS Atlas, PLTS, Rent-a-Tech, SDE, SML, Sourcebook America, SpatiaLABS, Spatial Database Engine, StreetMap, Tapestry, the ARC/INFO logo, the ArcGIS logo, the ArcGIS Explorer logo, the ArcPad logo, the Esri globe logo, the Esri Press logo, the GIS Day logo, the MapIt logo, The Geographic Advantage, The Geographic Approach, The World's Leading Desktop GIS, Water Writes, and Your Personal Geographic Information System are trademarks, service marks, or registered marks in the United States, the European Community, or certain other jurisdictions. CityEngine is a registered trademark of Procedural AG and is distributed under license by Esri. Other companies and products or services mentioned herein may be trademarks, service marks or registered marks of their respective mark owners.
3 Course introduction Introduction Course goals Additional resources Installing the course data Icons used in this workbook Understanding the ArcGIS Platform 1 Raster function chains and templates Raster functions Applying raster functions Image Analysis window basics Applying raster functions on a four-band raster Using a LAS dataset in a mosaic dataset Exercise 1A: Prepare a mosaic dataset for a function chain Create a folder connection Evaluate the LAS dataset Create a mosaic dataset Add the LAS dataset to the mosaic dataset Raster function chains Exercise 1B: Create the avalanche potential function chain Set up the raster function chain Build the function chain: Elevation range Build the function chain: Slope range Build the function chain: Aspect range Apply the function chain Export raster function template Reusing raster function chains Function template checklist Applying a function template to a raster Exercise 1C: Determine the avalanche potential of Elkhorn Peaks Apply the function chain template Export the results 2 Visually analyzing change over time Determining areas of change Sources of raster data Improving the display of raster data Satellite sensor data exploration Exercise 2A: Prepare the time-series mosaic dataset Create the mosaic dataset for the 2010 raster data i
4 Add the 2010 raster dataset to the mosaic dataset Evaluate input mosaic datasets Identifying areas of change Time-series analysis of Ikonos data Exercise 2B: Analyze the time-series mosaic dataset Create the master mosaic dataset for the time-series analysis Use the Time Slider to identify areas of change Identify areas of change 3 Introduction to image classification Demand for analytical change detection Exploring remotely sensed change Image classification history Types of image classification Classification workflows Remote-sensing processing workflow Classification outputs Planning your analysis 4 Change detection through unsupervised classification Unsupervised classification review Characteristics of coarse-resolution data Landsat bands Exploring Landsat data Exercise 4A: Use unsupervised classification to create a thematic raster Evaluate the Landsat data Modify the data Classify the data Identifying classes Identifying the classes on a Landsat raster Exercise 4B: Clean up results of unsupervised classification Prepare the data for identification Identify classes on the thematic raster Clean up identified classes Symbolize the generalized raster Comparing results against standards Exercise 4C: Measure the change Quantify the developed area Add the NLCD data Evaluate the NLCD data ii
5 Determine developed area change (Optional) Compare NLCD classifications 5 Supervised classification of developed areas Supervised classification review Components of a proper training sample Creating a spectrally pure training sample Exercise 5A: Prepare for supervised classification Determine classes in a study area Create training samples Evaluate training samples Create a signature file Evaluating your signature file Exercise 5B: Use supervised classification Apply the signature file Generalize to improve classification results Finalize supervised classification results 6 Accuracy assessment of classified results Using accuracy assessments Components of an accuracy assessment Accuracy assessment example Exercise 6: Assess the accuracy of classified results Create random points Evaluate random points Assess accuracy 7 Impervious surface analysis with object-oriented classification Object-oriented classification review Image segmentation Segmentation configuration Exercise 7A: Use image segmentation Apply the Segment Mean Shift function Modify segmentation settings Create a segmented raster Object-oriented training samples Creating object-oriented training samples iii
6 Exercise 7B: Measure an impervious surface with object-oriented classification Evaluate the segmented raster Create training samples Create a signature file for object-oriented classification Evaluate the object-oriented signature file Create an object-oriented classified thematic raster Reclassify the classified thematic raster Measure the amount of impervious surface 8 Applications of image analysis Analysis application Recommended workflows for the activity Appendixes Appendix A: Esri data license agreement Appendix B: Works consulted Appendix C: Answers to lesson review questions Lesson 1: Raster function chains and templates Lesson 2: Visually analyzing change over time Lesson 3: Introduction to image classification Lesson 4: Change detection through unsupervised classification Lesson 5: Supervised classification of developed areas Lesson 6: Accuracy assessment of classified results Lesson 7: Impervious surface analysis with object-oriented classification iv
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