1. Supervised Classification in Imagine D. Meyer dmeyer@usgs.gov E. Wood woodec@usgs.gov

2. Concept: Supervised Classification The goal of this exercise is to use the spectral signatures of different land covers to create a supervised classification. We will attempt to map the same land cover classes covered in the last exercise.

3. Geospatial data fundamentals Geospatial information types: – Raster: “images” composed of “pixels” – Vector: points, lines, polygons (“shapes”) Raster data types: – Continuous Single attribute (panchormatic = “black & white”) Multiple attribute (multi-spectral = “color”) – Discrete: Quantized continuous Categorical

4. Continuous vs. Categorical “Feature space” – set of all attributes describing an object. Student feature space: – Height (continuous) – Weight (continuous) – Hair color (weirdly continuous) – SSN (categorical) -doesn’t make sense to take an “average” SSN GIS attributes – Continuous – How warm? How bright? How much photosynthesis? What’s the mean population density? Crime rate per 100,000? – Discrete – what type of land cover? In which country is it located ?

5. Categorical – Land Cover

6. How to Classify Multispectral Images

7. RGB: decomposing images RGBredgreenblue ClassRedGreenBlue TomatoBrightVery dark BackgroundVery darkKinda darkMedium Green pepperKinda darkMediumVery dark Yellow pepperVery brightKinda brightVery dark Orange pepperVery brightKinda darkVery dark GarlicVery bright BowlMedium

8. RGB: spectral signatures ClassRedGreenBlue TomatoBrightVery dark BackgroundVery darkKinda darkMedium Green pepperKinda darkMediumVery dark Yellow pepperVery brightKinda brightVery dark Orange pepperVery brightKinda darkVery dark GarlicVery bright BowlMedium Bright Very bright Kinda bright Medium Kinda dark Dark Very dark RedGreenBlue

9. Supervised Classification Very widely used method of extracting thematic information Use multispectral (and other) information Separate different land cover classes based on spectral response, texture, …. i.e. separability in “feature space”

10. Supervised classification Want to separate clusters in feature space E.g. 2 channels of information Are all clusters separate? 10

11. Tools Identify spectral signatures of different land cover types using tools within Imagine: – Signature editor Alarm feature Signature editor statistics – Areas of interest (AOI’s) AOI tool – Supervised classifier (“maximum likelihood”) – Raster Attribute Editor

12. Supervised Landsat Classification Open “germtm.img” from the data folder (RGB=5,4,3)

13. AOI tool Open AOI -> AOI Tool Open AOI -> create polygons around training sites

14. Signature Editor Have the Classification menu open Utility -> inquire box and locate given x,y coordinates

15. Classify the image The goal of this exercise is to use the spectral signatures collected in the previous to classify the reflectance image: germtm.img (open this in a viewer, r,g,b->5,4,3) Open the previous AOI for germtm.img from the “spectral signatures” exercise. In the viewer menu bar: File-> Open-> AOI Layer to see the training polygons.

16. Input image with AOI’s

17. Classify the image In the Imagine Toolbar, click on the “ Classifier” button to get the Classifier menu; click on “Supervised Classification”

18. Classify the Image Input file: “germtm.img” Signature file: “germtm.sig” (from before) Output file: “germtm_sup.img” (in results folder for the current exercise) Parametric rule: Maximum Likelihood. Click “Okay”

19. Classify the image Open classified image in the same viewer as the input image (deselect “clear display”) Select the “Arrange Layers” icon in the Viewer and move the AOI layer to the bottom to hide the polygons (“Apply”).

20. Classify the image Swipe between the input and classified image. Move around and swipe between different areas to observe the results.

21. Refine the classification From the viewer window, select Raster->Attributes

22. Refine Classification In the raster attributes editor, click column properties icon to edit the location and size of the columns in the editor. Move the “Class Names” column heading to the “top” and change it’s wide to 10 (makes it leftmost column). Move the “color” heading “up” just below “Class Names”

23. Refine Classification Make various “classes” red to evaluate it’s accuracy (good urban classification)

24. Refine Classification Make various “classes” red to evaluate it’s accuracy (questionable urban classification)

25. Refine the classification One solution: delete the problem class in the signature file (iterate for all classes). Rerun classification with updated signatures.

26. Compare to Unsupervised classification Open “xiso.img” from the previous exercise (DO NOT CLEAR DISPLAY Use swipe to make a quantitiative comparison with germtm_sup.img Using the raster attributes editor, compute the number of pixels in each class for both the unsupervised and supervised classification