d of 60 
ne in the 
5, emplo- 
of detail, 
ind truth 
nd stored 
  
2. Digital methods 
The Hardware: The digital analyses of landsat MSS da7gand the geographical information 
system (GIS) interplays were done at the Michigan State University Center for Remote Sensing 
on the ERDAS 400 self-contained image processing microcomputer. 
Ihe LANDSAT Data set The study area occupies the extreme southeastern corner of the 
LAFIA scene (E—-2353 090125) of January 10 1976, with a zero percent cloud cover and qua- 
lity of 5588. 
A window of 600 x 200 pixels, corresponding approximately to the area covered by the 
soil map was extracted from this corner of the scene for digital processing. 
Supervised classification: A false colour composite (FCC) of this subscene was displayed 
using bands 4, 5 and 7. Making use of suitable enlargements, training sites were selected for 
signature records of the various physiographic—soil units. Training sites were either near the 
points of soil examination (auger bore—hole or profile) or an area known to have similar soil 
characteristics. Since the soils were not exposed at the time this Landsat data were acquired, 
the reflectances recorded would be of soil reflectances diluted by the vegetation reflectances 
or of entirely vegetation reflectances. 
Each physiographic—soil unit was sampled at least once, Where multiple training sites were 
taken, the signatures were pooled and the mean statistics — reflectances of the pixels on the 
training sites in each of the four MSS bands and covariance matrices calculated. These stati- 
stics are stored in a signature file, the subscene data were subjected to two types of Super 
vised classification: Maximum likelihood and mi nimu m distance. 
The maximum likelihood algorithm calculates the probability of a given pixel belonging 
to the same classas that of a training site. The assumption is made that there is a normal 
distribution of the cloud of points forming.the class, i. e. GAUSSIAN (Lillesand and Kieffer, 
1979). This 600 x 200 pixel window took 8 to 8% hours to run. 
The Minimum distance classifier, on the other hand, mathematically more simplistic, was 
at least 2% times faster than maximum likelihood approach. In this classifier, the Euclidean 
mean is calculated for each pixel. The mean spectral value is also calculated for each known 
category. An unknown pixel is classified by computing the distance between the unknown 
pixel and each of the mean spectral values for the categories. The unknown -pixel is assigned 
to the closest class, i .e. the class with the shortest distance to the pixel. In either case, the 
spectral curves have been plotted and each class has a known spectral response curve. 
Two pairs of classes — the Western sand island levee and the levee of the River Benue banks 
and the Awgu valley bottoms and the valley bottoms of the remaining uplands were merged 
and the signature file data subjected to maximum likelihood and minimum distance classifica- 
tions. 
Unsupervised classification: Unlike the supervised classification, the user does not interact 
with the computer to supply it with information classes. Rather, the pixels to be classified 
are unknown and the computer is to evolve algorithms that group the pixels into a priori classes 
on the basis of the spectral values of the image or data. The user supplies a few parameters such 
as the maximum number of clusters. The rule is that values in a given class are close together and 
the fairly distinct from other classes.