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the new coordinate system , the separability of the 
classes is described in terms of the different 
proportions of primary components that these 
classes present. 
4. EXPERIMENTAL RESULTS 
4.1 Methodology 
The experiment was performed over an area 
("ITAPEVA") that was reforested with  Eucalyptus, 
with different growing stages, in the state of Mato 
Grosso do Sul, Brazil. 
A Landsat TM image, taken on July gc 1984, with 
bands 1, 2, 3, 4, 5 and 7 was used. The digital 
numbers in the original bands were converted to 
reflectance, according to the procedure proposed by 
Markham and Baker (1986). Figures 4.1 displays the 
resulting bands, called respectively,  1R, 2R, 3R, 
4R, 5R and 7R. 
Aerial photographs taken approximately one month 
before the orbital coverage were available, The 
experiment was performed over a test area of 161 
rows by 161 columns, corresponding to the area 
covered by the aerial photographs. 
The first phase of the experiment was the synthetic 
bands generation, through the application of the 
computational methods that were mentioned in 
Section 2: Constrained Least Squares (CLS) and 
Weighted Least Squares (WLS). ^ 
The choice of the primary components was based on 
the work of Shimabukuro (1987). For this area, 
three components were considered: vegetation 
(eucalyptus), soil and shadow. The reflectance 
values of the vegetation and soil components were 
extracted from the image by Shimabukuro (1987), 
through the sample selection based on the available 
aerial photographs and reforestation map. The 
reflectance values of the shadow component were 
also obtained by Shimabukuro (1987), through the 
experiments performed by Heimes (1977), Figure 4.2 
presents the reflectance curves of the components. 
The analysis of the results obtained in this phase 
was qualitatively performed through the work 
previously performed by Shimabukuro (1987), since 
it is very difficult to obtain quantitative 
information from field work. 
The second phase of the work consisted of the 
comparative analysis of the Maximum Likelihood 
classifier under the gaussian assumption, through 
the conventional feature reduction methods 
described in Section 3 and the Mixing Model. 
Classification results were analyzed through the 
classification matrices generated from training 
samples. It is well known that the average 
classification performance estimated over these 
samples is optimistic but, since the objective of 
the present analysis is the comparison between 
different feature reduction methods, this fact was 
disregarded. Thematic images generated by the 
classification procedure were analyzed and 
qualitatively compared by using the available 
information. Unfortunately, it was not possible to 
reproduce here the thematic images, so they are not 
present in this work. They will be displayed in the 
poster presentation and in a future, work in 
preparation. 
4,2 Results 
261 
Phase 1 : Use of proportion estimators 
Synthetic bands derived from the proportions of 
eucalyptus (Vegetation Band), soil (Soil Band) and 
shadow (Shadow Band) generated by the CLS method 
are presented in Figure 4.3. Figure 4.4 presents 
the synthetic bands generated by the WLS method. 
Both methods present very similar results, are 
compatible with the available ground truth and are 
similar to the results obtained by  Shimabukuro 
(1987). Therefore, it was decided to follow the 
experiments using the synthetic bands generated by 
the CLS method. 
Visually, one can notice using the synthetic bands 
a more clear distinction between two types of 
eucalyptus. According to Shimabukuro (1987), this 
difference is due to age variations of the 
eucalyptus plantation. By analyzing the Shadow Band 
(Figure 4.3.c), it is possible to notice that one 
of this areas presents a higher shadow proportion, 
what means less uniformity and a higher age. 
On the basis of this results, the selected classes 
for the Maximum Likelihood classification were: 
New E.: reforestation with eucalyptus, with age 
between 8 months and 2 years; 
Old E.: reforestation with eucalyptus, with age 
greater than 2 years; and 
Soil : exposed soil. 
These classes can be discriminated on the basis of 
the different proportions of primary components, 
which indicate the structural characteristics of 
each class. As it can be observed in Figure 4.3, in 
class New  E., the pixels present a greater 
proportion of the Vegetation component. On the 
other hand, in class Old E., which is less uniform 
due to age, one can notice a larger influence of 
the Soil and Shadow components. Class Soil, as it 
was expected, is basically due to Soil component. 
Phase 2: Comparison between the Mixing Model and 
the Conventional feature reduction methods. 
Tables 4.1 to 4.4 present the classification 
matrices, when the following bands are used: 
a. The first three components (Cl, C2 and C3) 
generated by the Principal Components 
Transformation; 
b. The two components (Cl and C2) generated by the 
Canonical Analysis procedure; 
c. The three original bands (3R, 4R, 5R) selected 
by J-M Distance; and 
d. Synthetic bands derived from the proportions of 
primary components (Vegetation, Soil and Shadow 
Bands). 
In terms of average performance, the best results 
were obtained through the Canonical Analysis and 
the Mixing Model procedures. However, it should be 
considered that all methods present high values 
(and optimistic) for estimated average performance 
(higher than 92Z in all cases) and small variations 
from one case to another. Therefore, it is not 
possible to present a definite conclusion about the 
comparison in terms of classification performance. 
Through the qualitative analysis of the thematic