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The most powerful classifier in common use is that of Maximum Likelihood. Based on statistics (mean; variance/covariance), a (Bayesian) Probability Function is calculated from the inputs for classes established from training sites. Each pixel is then judged as to the class to which it most probably belong. This is done with the Morro Bay TM data, using first the six reflected radiation bands and then with the longer wavelength thermal band added. The result is a pair of quite believable classification maps whose patterns (the classes) seem to closely depict reality but keep in mind that several classes are not normal components of the actual ground scene, e.g., shadows. A later classification (made using IDRISI for Windows) using a smaller number of classes give a somewhat different end product.

### Maximum Likelihood Classification

We made this Supervised Classification using the Maximum Likelihood classifier acting on all seven bands. Again, multiband classes are derived statistically and each unknown pixel is assigned to a class using the maximum likelihood method.

1-23: While the minimum distance and this first Maximum Likelihood classifications are, for the most part, quite similar, there are some differences. Mention several. ANSWER

In this image we omit thermal Band 6 and define 16 classes (this is the maximum allowable in the IDRISI program). These classes are identical to the previous ones recorded in the Minimum Distance image. In both instances, the Sediment class has been subdivided into three levels (I and II in the ocean and a third in the Bay) and two Urban classes (I = Morro Bay; II = Los Osos) are attempted, to account for visual differences between them (mainly street patterns). Look at this image classification and decide how believable it is. Compare it with the minimum distance image as well. To assist you in comparing similar classes, we used the same color assignments. Next, look at a Supervised Classification that uses Band 6 and again specifies 16 classes. Notice how each urban area becomes more homogeneous. There is a similar increase in spatial homogeneity of vegetation and slopes in general with Band 6 added, but overall adding Band 6 didn't show much differences.

Each 16-class Maximum Likelihood version is a fairly dazzling image, with many classes "right on". Both Breakers and sand bar (Beach) seem uniformly classified. The sediment load distribution is credible. There are enough color tone differences between Morro Bay and Los Osos to justify the decision to make them two Urban classes (Los Osos differs in its street patterns and in the presence of the orange-brown soil, seen in the composite of Bands 1,2, and 3). However, color elements of one Urban class are mixed with the other, in differing proportions, as one would expect. The bright orange given to the coastal Marsh area occupies a slightly larger area than its equivalent does in the Minimum Distance classification and is also distributed in small patches around the Los Osos coastline, and again along the river. Thus it is probably a true condition, in that, we expect such vegetation to be more widespread. No doubt the most uncertain group of classes is spread over the hills. The categories SunLit Slope and Shadow Slope are somewhat artificial, in that they refer mostly to an illumination condition. Whereas the grass and trees classes may be a mix of lighting effects and a lighter or darker surface. The class Cleared Land is, again, a depiction of land surfaces that may support, not only thin natural vegetation or even be partially barren but also may in some places have a shadowing effect. The Grasslands is properly placed in this image but appears to spread over wider areas than indicated in several other images, so it is doubtless a valid case. The Green Vegetation category proxies well for the actual distribution of reflective organic material (in Band 4) but in this choice of class assignments, several types of growing ground cover are not singled out. Thus, elements of the golf course and the mountain crest forest are shown as "like" and are not distinguished from field crops, etc. We could tell them apart to some degree of correctness, if we had given each its own class and selected training sites.

Nearly two years after the above Supervised Classifications were generated, an occasion arose to redo the same scene using new IDRISI software that operates from Windows, Version 1, rather than DOS 3.1. In performing this Supervised Classification, we used the same Maximum Likelihood classifier with all seven TM bands and 15 classes. But, as an experiment, we decided to drop several class categories and select new ones instead. Also, we established some slightly different training site polygons for each class. In effect, we achieved an independent classification without peeking at the results, shown above, for guidance. And, instead of using the natural color scene from which to pick training sites, we used the false color image. This is the result:

(ERROR: For some reason, the Windows IDRISI does not show the 15th class in the legend. This class should be "Trees", in dark green, present mainly in the upper right corner of the class map. Also, the first legend box (black here; blue in the two other classifications above) has no label; it is not a named class but refers to the color used outside the map image as background.)

Note that for some of the classes, we assigned different colors than used in the first two maxlike classifications, which makes it rather difficult to compare the results with the earlier classifications. Nevertheless, scrolling between this and the 7-band, Supervised Classification just above, reveals differences and similarities.

In the Windows version, the two Sediment classes are combined. Also, the class, called Fields in the DOS 3.1 version, is here renamed GreenVeg, and includes fields with crops and also some natural vegetation (probably local woodlands). Both show bright red in the false color rendition. The distribution of the class Trees is similar in both classifications but is a bit more widespread in the Windows version (but harder to see because dark green and black shadows do not show contrast well). The classes Scrubland and Cleared in the DOS 3.1 version are partially represented by Scrub in the Windows version. In DOS 3.1, Urban II (focused on the Los Osos street pattern) is olive and is orange in the Windows version. In both cases, the distribution of the Urban II class pattern is much more extensive than is the real situation. Town structures or clusters of buildings do not exist in the long orange strip near the highway, nor in the lower right part of the image. Apparently, some natural surfaces, as interpreted from the true and false color composite images, give rise to signatures that resemble this urban class. In the Windows version, several very bright areas, mainly around Los Osos, have been named Sandpit. This is a guess, because they may be excavated ground or inland remnants of beach sand (although they classify as distinct from the Sand Class); only an on-site visit could ascertain a correct identity.

The point in running and comparing these two classifications is probably obvious: the precise end result is sensitive to the variables involved and the choices we made - mainly in extrapolating classes from their training sites to the identities and distribution of the selected classes, i.e., the overall appearance and accuracy of the classifications. Interpretations differ depending on the colors and other factors present in the training image, by which we choose separable classes and block efficient training sites. The number of classes, the validity (purity) of the enclosed space in the training sites (and the number of pixels in the polygons assigned to each class), the nature of a class (the Urban division is somewhat artificial and Scrub may be rather dissimilar classes or features in the real world), the colors assigned to the final map, and other considerations all contribute to differences. Once again, we emphasize the argument that field work, if logistically possible, before and after computer-based classification of an image, is the key to selecting and then checking class locations. Thus it is the best insurance for achieving a quality product. But, if an on-site visit is not feasible, a skilled interpreter can develop a fairly reasonable classification based mainly on his/her abilities in recognizing obvious ground features in the scene. The writer (NMS) has achieved believable classifications of many parts of the world without any field work, but just from his knowledge of the appearance of the common components of a landscape or land-use categories.

Primary Author: Nicholas M. Short, Sr. email: nmshort@ptd.net