Clustering and spectral interpretation is done for each of the major 
cover types independently before the analyst tries to assemble the results 
with the help of the LARSYS separability function. Clusters from spectrally 
similar major cover types are compared with this function which indicates 
statistical separability between all clusters. Some of those that conflict 
greatly are deleted at this stage if they represent two different types of 
land cover. Those clusters defining the same cover are pooled together if 
spectrally similar to form new clusters, the average of their components. 
The analysis establishes clusters for as many discrete land uses and 
covers as possible realizing that subsuming into broader but well defined 
classes is necessary in some instances. Thus, original clusters representing 
mostly new multi-family residences and those representing mostly new single 
family residences may eventually have to be subsumed into a class called new 
residential. The end product contains land-use classes that are quite 
discrete, sometimes beyond the Level II proposed by the U.S. Geological 
Survey for use with remotely sensed imagery (Anderson, Hardy and Roach, 
1972). The degree of detail possible seems to be a function of the geo 
graphic environment and the availability of enough sample areas on which 
to cluster. It was found that certain broad levels of land use are repli 
cable in all cases studied while those more discrete could perhaps be dis 
cerned only in areas where they were most pronounced. It was not possible 
to map older versus newer residential in Phoenix as it was in Springfield 
and Indianapolis only because the indicators (high density and/or accompany 
ing mature vegetation) that made such a distinction possible in the latter 
areas were absent. For final display, various subsumings of the discrete 
classes made the final map more readable by suppressing some of the detail. 
These groupings of the classes can be made to fit various user needs. 
The analysis proceeds in building block fashion -- clustering, spectral 
interpretation, checking separability, deleting, pooling -- until all clusters 
remaining have been checked with each other using the separability function. 
At this stage the function is utilized as a feature selector, providing the* 
analyst with a statistical idea of cluster separability using various subsets 
of the available spectral channels. In the Indianapolis work it was decided 
from this information that only five of the eight channels were needed: 
channels 1, 2 and 4 from January and 2 and 3 from September were selected as 
the all-round best combination. 
Throughout, test classifications are made to assess cluster suitability 
in various areas from throughout the test site. After a final set of clusters 
and channels is decided upon, a final check is made. Areas surrounding county 
boundary intersections are classified and checked. At the same time the line 
and column coordinates of those boundaries are found, providing information 
necessary for aggregating land use/cover by county. Computer cards are 
punched designating the ending column of each county encountered along each 
line as input for a modified version of LARSYS Printresults that then aggre 
gates the data by hectares and percentage.