Lab 5: Image Analysis with ArcGIS 10 Unsupervised Classification

Transcription

1 Lab 5: Image Analysis with ArcGIS 10 Unsupervised Classification Peter E. Price TerraView 2010 Peter E. Price All rights reserved Revised 03/2011 Revised for Geob 373 by BK Feb 28, V3 The information contained in this document is the exclusive property of Peter E. Price. 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 Peter E. Price. The information contained in this document is subject to change without notice. 1

2 As mentioned previously, unsupervised classification uses clustering routines to create the number of classes designated by the analyst. It is up to the analyst to assign meaningful identities to the classes after processing. A typical procedure would be for the analyst to specify a large number of classes and then refine and group them to produce features such as areas of land cover types. The unsupervised classification option provided by ESRI is the Iso Cluster Unsupervised Classification, also known as ISODATA (Iterative Self-Organized Data Analysis Techniques A) (Command 3 in the Classification toolbar dropdown menu). Unsupervised classifications usually do not have unclassified pixels if the parameters are set correctly. As mentioned in Lab 4, Iso Cluster Unsupervised Classification is the only unsupervised classification tool provided. It differs from the Iso Cluster tool in the Spatial Analyst Tools in that it creates an output classified image as well as a signature file. This command, however, does not offer control over the number of iterations used to define the clusters that is found in the Iso Cluster tool in the Spatial Analyst Tools > Multivariate functions as is shown below. You will now perform an unsupervised classification on the HouAirport_TM.tif image. Use the map document (.MXD) that you saved from the supervised classification. Clean the table of contents so that you only retain your base images and aerials, your unfiltered classification, and your best filtered result. Add the 2001 NLCD 1 layer (NLCD_2001.lyr) (downloaded from the Lab 5 web page) to the Table of Contents. Save As a new map document: Lab5.MXD. From the Classification toolbar, select the Iso Cluster Unsupervised Classification drop-down. Input HouAirport_TM.tif as the raster band. Specify the Number of classes as 24, name the output classified raster file HouAir24ISO, a Minimum class size (default, 20), a Sample interval of 1, and an Output signature file for future use. Your window should look like the example 1 NCLD: The USGS Multi-Resolution Land Characteristics Consortium s (MRLCC) National Land Cover Database (NLCD). 2

3 below. Given the small size of the study area, we can set the default Sample interval to 1. When working with larger files it may be necessary to set the sample interval to a greater number if the output contains fewer classes than requested it may be an indication that the sampling interval should be increased. The result should look something like the example below (the colours assigned to each class may differ). Note that the number of classes may differ from the 24 specified, and that some numbers may be skipped (e.g., dropped because that class didn t meet the minimum class size). 3

4 Compare the unsupervised classification with the MLC supervised classification you did earlier, the NLCD layer, the airport aerials and the original TM image. The unsupervised image looks noisy but it contains a lot of valuable information. As the analyst, it is now your job to make a useful classification from the unsupervised classes. Your assignment is to develop a limited set of classes that represent major groups of land cover. If you go to the USGS MRLCC site you can examine the 2001 NLCD classification scheme (one based on the Anderson scheme) A PDF of a report that documents the creation of the 2001 product is available here. If you open the attribute table of the 2001 NLCD layer you can see the correspondence between the class names and numbers: You will begin by assigning real identities to the ISO classes that were created. Use the TM image, aerials, and the NLCD product to help guide your interpretation. A good approach to this is to use your unsupervised classification as the top layer and Swipe it over the other layers. 4

5 An improvement on the plain swipe is to open the classification table of the ISO layer and select a class. This highlights a single class; it can now be compared with the underlying images and classifications. As shown in the example below, first changing the ISO symbology to a gray scale (1) allows the selected class (2) to be highlighted without visual clutter. Use the Identify tool (highlighted in the image below - 3) to view the class values in the underlying layers To improve your efficiency and workflow, first export the ISO classification table, place it in an Excel spreadsheet, and record your observations in the spreadsheet. To export the table, open the classification table and go to the drop down menu. Select the Export option and save the table as a.dbf with an appropriate file name. 5

6 Open the exported table in an Excel spreadsheet you will need to change the file type to All Files (*.*) in order for Excel to recognize the dbf. Save As the file to an.xlsx file. An example spreadsheet is presented below. You should add columns to the file in which to place the NCLD class numbers and the class names from your supervised (MLC) image, as well as a column for the ISO class names. Obviously some judgement will be required in deciding upon an ISO class name, as there will be multiple NCLD classes that fall within a single ISO class, and a single MLC 6

7 class will contain many ISO classes (and vice versa). You will need to identify the dominant class associated with each ISO class something where swiping will be an important contributor to your decision (e.g., you will need to ignore small groups of NCLD and MLC pixels within an ISO class). In order to simplify the comparisons amongst the multiple datasets, use the ISO class names listed below, and follow the equivalences listed between the classes used in the supervised classification (the Maximum Likelihood Classification) and the NLCD 2001 classes (class numbers and names are presented here). The numbers in brackets (##) indicate that that class is present but typically in lower numbers, relative to the other classes. Note, however, that you will find just about every combination of ISO class / MLC class / NLCD class occurs, although the numbers of cells involved should typically be very low. MLC CLASS # MLC CLASS NAME NLCD CLASS # ISO CLASS NAME (DERIVED FROM THE NLCD CLASS NAMES) ISO RECLASS # 1 Dry grass 21, 22, (23) Developed, Open space 1 2 Forest 21, (22), 41, Forest 2 42, 43, (71), 90 4 Parking lot (22), (23), 24 Developed, High intensity 4 5 Building roof (21), 22, 23, Developed, Medium Intensity Green grass 21, 22, 23, (24), 42, (71) Developed, Low intensity 6 The ISO reclass numbers will be used to simplify the 22 ISO classes into 5 classes directly compatible with the MLC classes. I ve also ordered them so that they roughly correspond to the MLC scheme. You should note that the NLCD class names are land-use oriented rather than land-cover oriented (as are the MLC class names). You can see from the incomplete spreadsheet example presented below that, as expected, there are multiple ISO classes associated with a single ISO class name. Merging some ISO classes that have obvious relationships is needed. As shown in the example below, ISO classes 1 and 3 appear to be primarily associated with forest (ISO reclass # 2), classes 4 and 16 correspond primarily to Developed, Open space (ISO reclass # 1), and classes 9 and 10 to Developed, Low intensity (ISO reclass # 6). Once you have completed the table, the 24 ISO classes will need to be reassigned into a smaller set of comprehensive classes using the Reclassify tool. To make the reclassification exercise easier, you should add a column to the spreadsheet wherein you will place the reclassified numbers (ISO Reclass number) as given above (and as shown in the example spreadsheet below). 7

8 Once you have completed your assessment of every ISO class, open the Reclassify tool by going to ArcToolbox > Spatial Analyst Tools > Reclass > Reclassify. This will open the Reclassify menu seen below. The input is the HouAir24ISO layer. If the classes do not appear, be sure the Reclass field is Value and click the Unique button. Enter the new class designations in the New values boxes. Save the completed reclassification to an appropriate file name such as HouAir6ISO. 8

9 If needed, add the reclassified image to the Table of Contents. Set the symbology to display the new classes (match the colour scheme used in your MLC image) and label them appropriately. Your results should look something like the image below. You should clean up the ISO results following the protocol you used in Lab 4 (e.g., Majority Filter, Boundary Clean). Use your skills and the tools you have explored to create a map that represents the major land cover types in this area. Place the map in a Word document with an explanation of your process and the results. Discuss the differences you observe between the NLCD layer, your MLC results, and your ISO classification results. In order to more formally compare your MLC classification to the ISO classification, open the attribute tables of both images (the final images that have been filtered and cleaned) and prepare a summary table similar to the one presented below (include both the raw pixel counts as well as the relative percentages for each class). Class # MLC Class Name MLC Count % ISO Class Name 1 Dry grass Developed, Open 2 Forest Forest 4 Parking lot Developed, High 5 Building roof Developed, Medium 6 Green grass Developed, Low ISO Count % 12

10 A (pseudo) Accuracy Classification A final step in most image classification projects would be an accuracy assessment and the creation of a confusion matrix. As mentioned in class, the proper way to conduct such an assessment would be to collect ground truth in this case, using the higher-resolution aerial photos to provide an assessment of what the land cover actually is. To do this with some statistical rigour, we need to create a number of randomly positioned points throughout the classified image, assign the ISO class name to each point, and then determine through visual inspection at each point what the true land cover is. Once all of the points have been ground truthed, we can create a confusion matrix and calculate the relevant statistics that will tell us how well our classification did. ESRI has provided a number of tools within the Segmentation and Classification toolset (found within the Spatial Analyst set of tools) that automate most of the accuracy assessment / construction of the confusion matrix process. I will lead you through the steps of completing an accuracy assessment, but for the purposes of this lab we will make the assumption that the results of your supervised classification are the truth to which we will compare the results of the ISO unsupervised classification. Step 1) Create the Accuracy Assessment Points: Select your final ISO file (after being filtered and cleaned) as the input raster, provide an appropriate output file name (this will be a point file, it can be either a shapefile or in a geodatabase), and accept the defaults for the other fields (the Target Field should be classified since we want to know what classes in the ISO image are associated with each stratified randomly placed point). You should see a point file containing 500 points scattered about the ISO image. Open the attribute table to see what the file contains. 12

11 Step 2) Update the Accuracy Assessment Points: Select your final MLC image as the Input Raster, the point file you just created in step 1 is the Input Accuracy Assessment Points file, provide an appropriate name for the output file, and change the Target Field to Ground_Truth. Open the attribute table of the output accuracy assessment points file and note that it now contains attributes from both the classified image (the ISO image) and the GrndTruth image (the MLC image). You now need to compute the confusion matrix. Step 3: Create Confusion Matrix: This tool takes the accuracy assessment results and creates a confusion matrix (basically it creates a pivot table [Classified as the rows, GrndTruth as the columns, and the counts as the values] and then calculates a number of statistics from that pivot table). 12

12 You final results should look something like that presented below, although everyone s results will look slightly different (based on the random placement of points, and of the differences in everyone s classifications). Given the order in which we specified the classification and the ground truth, the rows represent the ISO classes and the columns the MLC classes. If you selected the.dbf option you will need to format the cells (removing the decimal places for those cells that are simple counts, and setting the number of decimal points to 5 for those that are statistics) so that your table looks similar to that presented above. Based on the results of your (pseudo) accuracy assessment, which ISO class was the most accurately classified (looking at both the Producer s and the User s Accuracy values), and which was the most poorly classified? What was the Percent Correctly Classified, and what is the Kappa coefficient, for your results? Include a copy of the accuracy assessment in your report. You should provide the proper class names in your table (e.g., row C_1 should be Developed, Open space while column C_1 should be Dry Grass). In your final project you will be required to produce both an unsupervised and a supervised classification of a Landsat scene that you will personally download, and provide similar details / discussion as outlined in your labs. 12