Accordingly, a need was expressed to cluster and classify on a data set 
composed of two contrasting season ERTS-1 passes, overlain and registered. 
Clustering, as explained above, was done using eight channels of data. In 
the first test application, the Phoenix metropolitan area, results using 
the overlain dates of 16 October 1972 and 2 May 1973 showed a marked im 
provement in rural-urban separation over a single date classification done 
earlier (figure 1). Success in the Phoenix area prompted use of overlain 
temporal data for both Springfield and Indianapolis. Two dates are also 
to be employed in planned work on Washington, D.C. and three dates have 
been overlain for a test in San Jose, California. 
Selection of optimum dates requires consideration of the crop calendar 
and the vegetative cycle found in each test site. In humid areas of the 
east, with relatively short and distinct growing seasons, one summer date 
reflecting mature crops and one winter season date (non snow covered) with 
defoliated trees makes for an optimum selection. In Indiana, for example, 
the disparity in values in ERTS Band 6 (Channel 3) between corn and soy is 
greater in mid-growing season than it is at the end (July rather than late 
September). An earlier date with more exposed soil ambiance is equally 
unsatisfactory. 
Even with multi temporal data, some confusion between rural and urban 
persists. Some remains as a real problem whose solution may have to await 
introduction of a new tool such as texture analysis, but other confusion is 
simply an expression of the generalization within the resolution element, 
the character of the sampling where mapping is done at less than full reso 
lution, or, as in the case of sparse urban-fringe settlements, just too 
rural-like to make the same sort of conceptual generalizations made by the 
mind as the eye looks at an air photograph. 
Seeking a solution to rural-urban separation by using multitemporal 
data resulted in the serendipitous discovery that sharpening the distinction 
between troublesome spectral clusters often resulted in the uncovering of 
numerous class sub-types. For instance, distinction between some discrete 
crop types (corn from soy) and some urban sub-types (older from newer resi 
dential) were made. Thus, in the process of making distinctions between 
generalized classes -- agriculture versus urban -- more subdivisions of 
each were identified, meaning that penetration of a whole new level of 
discreteness has been made, one far beyond any land-use applications of 
ERTS-1 envisioned earlier. 
The maximum and optimum number of land-use classes to be identified 
through spectral interpretation is as yet unknown. While the photo inter 
preter uses all the traditional aides of shape, color, and areal association, 
the spectral interpreter must make all his identifications from spectral data, 
digital surrogates for reality. The art of spectral interpretation is not yet 
well enough developed to know the limits of the maximum number of classes 
possible. Promise is held for such distinctions as the quality of housing and 
the nature of open space.