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Supervised Classification

Supervised classification is much more effectual in terms of accuracy in mapping substantial classes whose validity depends largely on the cognition and skills of the image specialist. The strategy is simple: conventional classes (real and familiar) or meaningful (but somewhat artificial) classes are recognized in the scene from prior knowledge such as personal experience with the region in question, or by identification using thematic maps or actual on-site visits. This allows one to choose and set up discrete classes (thus supervising selection) to which identifying category names are then assigned. Training sites, areas representing each known land cover category that appear fairly homogeneous on the image (as determined by similarity in tone or color within shapes delineating the category), are located and circumscribed by polygonal boundaries drawn (using the computer mouse) on the image display. For each class thus outlined, mean values and variances of the DNs for each band used to classify are calculated from all pixels enclosed in the site(s) (more than one polygon can be established for any class). When DNs are plotted as functions of the band sequence (increasing with wavelength), the result is a spectral signature or spectral response curve for that class (in reality for the assemblage of materials within the site that interact with the incoming radiation). Classification now proceeds by statistical processing in which every pixel is compared with the various signatures and assigned to the class whose signature comes closest (a few in a scene do not match and remain unclassified; these may belong to a class not recognized or defined).

Many of the classes to be constituted for the Morro Bay scene are almost self-evident - ocean water, waves, beach, marsh, shadows. In practice, we could further sequester several such classes, as for example, distinguishing between ocean and bay waters, but their gross similarities in spectral properties would probably make separation difficult. Other classes that are likely variants of one another - such as slopes that either face the morning sun as Landsat flew over versus slopes facing away - might be warranted. Some classes are broad-based, being representative of two or more related surface materials that might be separable at high resolution but are inexactly expressed in the TM image: in this category we can include trees, forest, and heavily vegetated areas (golf course; farm fields).

For the first attempt at a supervised classification, 13 discretional classes have been formalized. The outlines of their training sites are traced on the true color (bands 1,2,3) composite, as shown (their site colors are assigned here for display convenience and do not correspond to their class equivalent colors in the maps shown on the next page).

Note that Idrisi does not actually name them (they are numbered and given names [tied to the numbers]) during the stage when signatures are made. Several classes gain their data from more than one training site. Idrisi has a module, SIGCOMP, that plots the signature of each class. Here we show plots for clear seawater (light blue) and water with three different sediment densities (green, brown, blue-green) and surf waves (yellow-green).

It also has a program that presents pixel information for each signature, recording the number of pixels contributing to the data, and the mean, maximum, minimum, and standard deviation of DN values for each signature. To help you get a deeper feel for the numerical inputs involved in these calculations, we have reproduced a simplified version of these data in the following table:

 

Table of Band Means and Sample Size for Each Class Training Set

BAND: 1 2 3 4 5 6 (TH) 7 No. of

Pixels
Class                
1. Seawater 57.4 16.0 12.0 5.6 3.4 112.0 1.5 2433
2. Sediments1 62.2 19.6 13.5 5.6 3.5 112.2 1.6 681
3. Sediments2 69.8 25.3 18.8 6.3 3.5 112.2 1.5 405
4. Bay Sediment 59.6 20.2 16.9 6.0 3.4 111.9 1.6 598
5. Marsh 61.6 22.8 27.2 42.0 37.3 117.9 14.9 861
6. Waves Surf 189.5 88.0 100.9 56.3 22.3 111.9 6.4 1001
7. Sand 90.6 41.8 54.2 43.9 86.3 121.3 52.8 812
8. Urban1 77.9 32.3 39.3 37.5 53.9 123.5 29.6 747
9. Urban2 68.0 27.0 32.7 36.3 52.9 125.7 27.7 2256
10. Sun Slope 75.9 31.7 40.8 43.5 107.2 126.5 51.4 5476
11. Shade Slope 51.8 15.6 13.8 15.6 14.0 109.8 5.6 976
12. Scrublands 66.0 24.8 29.0 27.5 58.4 114.3 29.4 1085
13. Grass 67.9 27.6 32.0 49.9 89.2 117.4 39.3 590
14. Fields 59.9 22.7 22.6 54.5 46.6 115.8 18.3 259
15. Trees 55.8 19.6 20.2 35.7 42.0 108.8 16.6 2048
16. Cleared 73.7 30.5 39.2 37.1 88.4 127.9 45.2 309

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Code 935, Goddard Space Flight Center, NASA
Written by: Nicholas M. Short, Sr. email: nmshort@epix.net
and
Jon Robinson email: Jon.W.Robinson.1@gsfc.nasa.gov
Webmaster: Bill Dickinson Jr. email: rstwebmaster@gsti.com
Web Production: Christiane Robinson, Terri Ho and Nannette Fekete
Updated: 1999.03.15.