Understanding MultiSpec and QGIS for Image Analysis

BIA4100: Location Analytics Exercise 3: MultiSpec Exercise Version: SP2021b Exercise Author: Erskine Exercise Editor: Johnson Credits: Original Lab Content Author Authored by Kurt Menke, GISP; Content Authored by Richard Smith, Ph.D., GISP Texas A&M University - Corpus Christi; The development of the original document was funded by the Department of Labor (DOL) Trade Adjustment Assistance Community College and Career Training (TAACCCT) Grant No. TC-22525-11-60-A-48; The National Information Security, Geospatial Technologies Consortium (NISGTC) is an entity of Collin College of Texas, Bellevue College of Washington, Bunker Hill Community College of Massachusetts, Del Mar College of Texas, Moraine Valley Community College of Illinois, Rio Salado College of Arizona, and Salt Lake Community College of Utah. Objective: Explore and Understand How to Display and Analyze Remotely Sensed Imagery Introduction In this exercise, you will learn how to display and inspect multi-band imagery in QGIS Desktop. They will use QGIS data processing tools to conduct an unsupervised classification of multi-spectral imagery. They will then use MultiSpec to perform a more advanced analysis. MultiSpec is a freeware multispectral image data analysis system created at the Purdue Research Foundation. MultiSpec provides the ability to analyze and classify imagery data, among other tasks. This lab has been adapted from four tutorial exercises provided by the MultiSpec team and introduces the software package. This exercise includes the following tasks: • Task 1 – Display and Inspection of Image Data • Task 2 – Supervised Classification Required and Recommended Tools QGIS, MultiSpec Objective: Learn the Basics of using QGIS Desktop and MultiSpec for Image Analysis

Image analysis is one of the largest uses of remote sensing imagery, especially with imagery that has recorded wavelengths beyond the visible spectrum. There are proprietary software packages designed specifically for remote sensing work such as ENVI and ERDAS Imagine. QGIS Desktop can now be used in combination with two additional FOSS4G software's, SAGA and GRASS, to also conduct image analysis. SAGA and GRASS are both standalone software packages that can be installed separately. However, the main analysis tools from both are now bundled with QGIS Desktop. This means that no additional installations are required in order to use GRASS and SAGA analysis tools via QGIS Desktop. However, some of this functionality is for more advanced users. For this reason, you will also learn how to use MultiSpec which is a very simple and intuitive, but powerful, freeware image analysis software package. This lab was adapted from the first four tutorials provided by the MultiSpec team. Task 1: Using data dictionaries and attribute selections There are many ways to view multi-band image data. Here you will explore some display options for a multi-band image in QGIS Desktop. 1. Open QGIS Desktop. 2. Click the Add Raster Layer button and navigate to the ex3data folder. Set the filter to All files (*)(*.*). 3. Select the file named ag020522_DPAC.img and click Open. This raster layer does not have a defined coordinate reference system (CRS). Therefore, QGIS opens the Coordinate Reference System Selector window. If this window does not open automatically, double-click the layer and click the Select CRS button under the General tab. This interface lets you define the CRS before the layer is added to the Layers panel. This raster is in UTM, zone 16, WGS84. 4. Type ‘zone 16’ into the Filter window. In the Coordinate reference systems of the world box you’ll see a list of all the CRSs with zone 16 in the name. Scroll through until you find WGS/84 UTM zone 16N (EPSG: 32616). Select it so that it appears in the Selected CRS box (see figure below) and click OK .

Coordinate Reference System Selector 5. The image will be added to QGIS (shown in figure below). This is an aerial photograph of a portion of the Davis Purdue Agriculture Center in Randolph County, Indiana.

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Multi-band Image in QGIS Desktop 6. Save your QGIS Desktop project to your lab folder as ex3.qgs 7. Double click on the layer name in the Layers panel to open the Layer Properties. 8. Click on the Symbology tab. When an image has multiple color bands, QGIS defaults to a Multiband color rendering of that image. Colors on your computer monitor are created by combining three color channels: red, green and blue (RGB). By selecting three bands from a multiband image, and illuminating them with either red, green or blue light we create a color image. The multiband color renderer defaults to displaying Band 1 through the red channel, Band 2 through the green channel and Band 3 through the blue channel. However, we can change which bands are displayed through which channels. 9. Click the drop-down arrow for the Red band and change it to Band 3. Change the Blue band to Band 1 (see figure below).

Changing the band combination in QGIS 10. Click Apply and move the Layer Properties window so you can see the raster. Note : The difference between using Apply and using OK. Clicking OK saves the changes and closes the dialog window. Apply saves the changes and leaves the window open. If you want to change a setting, see the result and change another setting use Apply. 11. The image should now look like the figure below. This band combination creates what is known as a false color composite. Vegetation reflects a lot of near-infrared energy. You are now looking at the near-infrared through the red channel so vegetation shows up as red tones. The brighter the red, the more vigorous and healthier the vegetation. False Color Composite The Symbology tab also allows you to adjust Contrast enhancement. This setting gives you options to modify the appearance of the image when used in combination with the Load min/max values settings. Each band has values

from 0-255. By default, the renderer is set to use Cumulative count cut values from 2% to 98%. This setting eliminates the bottom and top 2% of the values. Many images have some outlying very low and high data values. These outlying data values can be eliminated by using the Cumulative count cut option. The Contrast enhancement is set by default to No enhancement. 12. The values currently being used for each band will appear in the Min/max boxes in the Band rendering area. 13. Change the Contrast Enhancement to Stretch to MinMax and click Apply. MinMax Stretch The Accuracy setting lets you either estimate the range of values from a sample or get the actual values. Obtaining actual values can take longer since QGIS has to look at all the values in the image, instead of a sample. 14. Now choose a Load min/max values setting of Mean +/- standard deviation to specify within 1 standard deviation and click Load. Click Apply to see the image change. 15. Change the Accuracy setting to Actual, and click the Load button to see the values change slightly. The raster gets a more saturated appearance (shown in figure below). These are the values within one standard deviation of the mean value. This is useful when you have one or two cells with abnormally high values in a raster grid that are having a negative impact on the rendering of the raster.

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Image rendered with Mean +/- Standard Deviation 17. In the Layer Properties, click on the Transparency tab. 18. With the Global transparency setting you can control how transparent the entire image is. 19. You can also define image values that you want to be transparent. Notice that in the southwest corner there is a black rectangle with no image data. On the Transparency tab click the Add values from display button then click on the black rectangle on the map. QGIS will measure the values for all three bands where you clicked and enter them into the Transparent pixel list. 20. Click Apply. The black rectangle of no data pixels disappears. Raster With Transparency

24. Click on the Histogram tab. Here you can view the distribution of data values in your raster. If it is a multi-band image, you can view data for each band. Image Histogram 25. Save your QGIS Desktop project. Task 2 - Supervised Classification In a supervised classification, you choose the areas that to be used as training areas for the analysis. Training areas should be a homogeneous sample of a particular feature, such as the forested area in the ag020522_DPAC image. For each training area, the multi-spectral (multi-band) pixel values are extracted and used to define a statistical signature. This signature is a statistical representation of a particular class, which is used by the software identify all pixels with a similar signature. 1. Open MultiSpec 2. From the menu bar choose File | Open Image. Choose ag020522_DPAC.img.

3. The Set Display Specification for: window opens. Take all the defaults and click OK. MultiSpec should now resemble the figure below. There are two windows the image view and text output. You can arrange them so that you can see both at the same time. MultiSpec

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5. From the menu bar, choose Project | New Project. Click OK. The Project window will open. 6. Now you will begin to select training areas. Again, these will be homogeneous areas. Start with the forested area. Simply drag a small rectangular area within the forested portion of the image. Note that in the Project box the coordinates (row and column numbers) of the upper left corner and the lower right corner of the selected area appear the box near the bottom. Click Add To List the Define Class and/or Field Description window opens. Note : Be careful not to define your training areas near the edges of the field or forest. You should stay away from edges by a couple of pixels to reduce the chance of edge affects. If upon inspection, you do not like the exact area chosen, you can immediately repeat the process. 7. Enter the class name of ‘Trees’ and click OK (see figure below). Define Training Class 8. You will define 5 additional training areas. Refer to the figure below. They will be named: ‘Wheat’, ‘Soil’, ‘Light Soil’, ‘No Data’ and ‘Weeds’.

Six Training Areas 9. In the Project window click the >Classes button so you see all six of your classes (see figure below).

Project Window Showing Classes 10. If necessary, you can delete a class by selecting the class in the list and selecting Cut Class from the Edit menu. 11. From the menu bar choose Processor | Classify to open the Set Classification Specifications dialog window. 12. Uncheck Image Selection. The other default settings are fine. Click OK (see figure below). If prompted to ‘Update project statistics before continuing’, click OK. Set Classification Specifications For Training Area Evaluation Since Image selection was unchecked, MultiSpec only estimated the quality of the training areas. You can examine the tabular output to assess if the full analysis should be run. 13. Look at the Text Output window. The TRAINING CLASS PERFORMANCE table tabulates how the pixels of each field and class were classified. The Reference Accuracy (%) should be near 100% for all training fields (see figure below).

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Tabular Output of Analysis 14. Assuming satisfactory results, you are ready to classify the entire area. 15. From the menu bar choose Processor | Classify to open the Set Classification Specifications dialog window. 16. Set the Classification Specifications as follows (shown in figure below): a. Uncheck Training (resubstitution) b. Check Image Selection c. Check Disk File under Write classification results to: so that a disk file of the analysis will be created. d. Check Create Probability Results File e. Click OK.

Set Classification Specifications for Full Analysis 17. The analysis will run. Shortly you will be prompted to save two resulting files. Take the defaults and click OK both times (see figure below). Save Supervised Classification Results

18. From the menu bar choose File | Save Project as ex3.prj 19. To see the results choose File | Open Image from the menu bar. 20. Set the Files of type: as Thematic ( .gis; .tif; *.clu). 21. Select the ag020522_DPAC.gis file and click Open. The Set Thematic Display Specifications window opens. Click OK to take the defaults. The results look much more realistic than those obtained from the unsupervised classification! 22. To see the training areas superimposed on top of the results go to the menu bar and choose Project | Add as Associated Image. 23. To change the training field outline color, go to the menu bar and choose Processor | Statistics. The Set Project Options window opens. Under Outline selected areas choose Color: black (see figure below). Supervised Classification Results 25. Now you will bring the MultiSpec supervised classification results into QGIS Desktop. If necessary Open QGIS Desktop and from the menu bar choose Project | Open Recent | Lab 6.qgs. 26. Click the Add Raster Layer button and choose the ag020522_DPAC.gis file. 27. Right click on the layer in the Layers panel and choose Set Layer CRS from the context menu. 28. Choose WGS84/UTM zone 16N from the Recently used coordinate reference systems. The layer should overlay your image. 29. Open the Layer Properties for this layer and go to the Symbology tab. 30. Choose the following rendering options (reference figure below):

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a. Render type: Singleband pseudocolor b. Choose Random Colors for the color ramp c. Choose Equal Interval as the Mode. d. Set the number of Classes to 6. e. Click the Classify button and the six classes will receive unique colors. f. Change the Values to 1-6 to match the class values in the data. Double click on the values to edit them. g. Change the Labels to match the six classes. Double click on the values to edit them. h. Click on the individual color patches and change them to more intuitive colors. Styling the Supervised Classification Data in QGIS Desktop

Your data should now resemble the figure below (not required to be the same color scheme). Supervised Classification Data in QGIS Desktop Conclusion In this exercise, you learned the basics of working with multi-spectral imagery in QGIS Desktop. You learned how to access data processing tools in QGIS Desktop and how to do a Supervised Classification in MultiSpec, a freeware multispectral image data analysis system. MultiSpec is an excellent example of many free-to-use programs available for imagery analysis and GIS-related tasks. Submission Please submit your work using a Microsoft Word file- You may just screenshot your work in QGIS and add it to a Word file. Please do not submit your QGIS project files to the Dropbox. Additionally, please in your own words summarize what you did in this exercise. Provide an example of where you could apply these steps in the future. Please limit your response to less than 250 words and include it in the same Word document as your screenshots.

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