In practice, we could further sequester several such classes. For example, we might distinguish 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 faced the morning sun as Landsat flew over versus slopes that face away, might be warranted. Some classes are broad-based, representing two or more related surface materials that might be separable at high resolution but are inexactly expressed in the TM image. For the first attempt at a Supervised Classification, we show 13 discretional classes.
Several classes gain their data from more than one training site.
The ordinate values range from 0 to 255. The greatest separability among all classes occurs in Band 5. 7 No. of Pixels Class 1. Band 6 is much smaller than in other bands, suggesting its limitation as an efficient separator. However, as we will see next, its addition to the Maximum Likelihood Classification increases the spatial homogeneity of some classifications.