——— MM —— —————— 
  
  
images (not present in this work), one concludes 
that the best results were obtained by Canonical 
Analysis, followed by the Mixing Model. A clear 
improvement in terms of non-classified pixels can 
be observed on these two images. 
By analyzing the thematic image that results from 
the use of synthetic bands, it is possible to 
notice the highest concentration of non-classified 
and confusion between New E. and Old E. classes 
occurs over a stripe located in the area where the 
new eucalyptus are planted. As it can be observed 
in Figure 4.3, these pixels present a greater 
proportion of the Soil component. This fact makes 
them similar to the pixels that belong to Old E. 
class, in terms of proportions. This structural 
characteristic is not so clearly perceived when 
other methods are used. 
The reason for this structural difference found 
within the area of New E. class is beyond the scope 
of the present work. However, a deeper study of 
forest engineering could be performed, by using 
additional information about the canopy structure 
provided by the primary components proportions. 
Table 4.1 Classification matrix obtained when the 
first three Principal Components (Cl, C2, C3) were 
used. 
  
Not ; 
Classified New BE. ^ Old F. Soil 
  
New E. 3.7 96. 0.3 0.0 
Old E. 2.7 . 95.7 0.7 
Soil 5.9 1.9 92.2 
7 
Average Abstinence: 3 
Average Confusion: 
6.0 
1.0 
0.0 
Average Performance: 94,5 
4,1 
1.3 
  
  
  
Table 4.2 Classification matrix obtained when the 
Canonical axes (Cl, C2) were used. 
  
Not 
Classified New E. Old E. Soil 
  
New E. 3.3 96.7 
Old E, 4,7 0.0 
Soil 2.2 0.0 
Average Performance: 96.0 
3.3 
0.5 
Average Abstinence: . 
Average Confusion: 
TOM Mo 
  
  
  
Table 4.3 Classification matrix obtained when the 
original bands selected by the J-M distance (3R, 
4R, 5R) were used. 
  
Not 
Classified New E. Old E. Soil 
  
New E. 5.3 94.3 0.3 0.0 
Old E. 3.0 0.3 96.3 0.3 
Soil 5.6 0.0 2.2 92.2 
Average Performance: 94.24 
Average Abstinence: 4.67 
Average Confusion: 1.09 
  
  
  
Table 4.4 Classification matrix obtained when the 
synthetic bands (Vegetation, Soil and Shadow Bands) 
were used. 
  
Not 
Classified New E. Old E. Soil 
  
  
  
New E. 2.5 95.6 1.9 0.0 
Old E. 1.0 0.3 97.0 1.3 
Soil 1.0 0.0 1.7 97.3 
Average Performance: 96.74 
Average Abstinence: 1.52 
Average Confusion: 1.74 
  
4.3 Discussion 
The results that were obtained with the "ITAPEVA" 
experiment demonstrate that the Mixing Model can be 
used as an alternative method for the feature 
reduction phase of the classification process: its 
average performance was comparable to conventional 
methods and, furthermore, additional information 
about the structural characteristics of the classes 
was obtained. 
It is important to point out that the methods 
compared in this work aim at different objectives, 
as it is described in the following. 
The most straightforward method is the feature 
reduction by the J-M Distance. The advantage of 
this method is to be an indicator of the 
performance of the Maximum Likelihood classifier, 
if the gaussian assumption is verified. 
The Principal Components transformation aims at the 
optimum representation of the mixture of the 
classes in the mean square error sense. The usual 
choice is for those axes that have the largest 
variances. However, for classification, the main 
objective is class discrimination and not 
representation (Duda and Hart, 1973). According to 
Richards (1986) the good classification results 
obtained with the Principal Components 
transformation can be credit to the fact that 
Remote Sensing classes usually are spread over the 
first principal components axis. This is 
particularly so for soils and spectrally similar 
cover types. 
On the other hand, Canonical Analysis aims 
specifically at the discrimination. If the number 
of classes, k, is less then the original feature 
dimensionality, p, then the maximum number of 
features generated by this method is (k-1). The new 
features can be used as more adequate inputs to the 
classification process. Another important 
application is the use of the color composite of 
the canonical axis, which were found to be very 
useful for the visual interpretation in 
heterogeneous areas, since the new features tend to 
maximize the differences between soil categories 
(Mather, 1987). 
The use of the synthetic bands can also be very 
useful for the visual interpretation of class 
distributions. Qualitatively, their color composite 
(not present in this work) is even better than the 
color composites obtained by the other methods for 
visual discrimination between the classes. 
Furthermore, with the use of the synthetic bands, 
classes are not described as a function of their 
spectral response (spectral signatures) but in