narrow or absent. Real SPOT imagery of the United 
Kingdom would be recorded in the morning and would 
contain larger shadows, which are expected to be use 
ful in detecting hedgerows. 
2.2 Preprocessing of simulated imagery 
The preprocessing of the simulated imagery included 
two image rectification techniques applied to the raw 
data recorded by the Daedalus scanner (Hunting Geology 
and Geophysics Ltd. 1984). One was a linear scaling 
technique by which the swath was scaled to fit a map. 
The second technique was an S-bend correction which 
rectified distortions caused by the radial rotation of 
the scan mirror. This latter correction is unnecessary 
in data from push-broom scanners like those on board 
the SPOT-1 satellite. 
During the course of the project, it was discovered 
that the three simulated multispectral bands were not 
properly registered to one another. Whether this prob 
lem is due to preprocessing or subsequent processing 
is presently unknown. The misregistration was verified 
by two methods! 
1. By locating the edges of obvious features in 
printed arrays of DN values and comparing the loca 
tions in each spectral band. 
2. By visually comparing registered and unregistered 
colour-composite images on adjacent display terminals. 
This misregistration was corrected by shifting band 
1 one pixel to the west and by shifting band 3 one 
pixel to the east. 
3 PREVIOUS METHODS FOR CLASSIFYING MIXED PIXELS 
Figure 1. Classification zones in a hypothetical 
feature space. 
training set 
mixed pixel 
region 
training set 
In the literature, there are two types of methods for 
classifying mixed pixels. In the first type, mixed 
pixels are treated as whole entities and are assigned 
to a single class. It is assumed that the class as 
signment corresponds to the dominant constituent 
class. Textural (Haralick 1979) or contextual informa 
tion (Gurney & Townshend 1983) may be incorporated in 
to the classification procedure. 
The second type of classification method for han 
dling mixed pixels involves pixel splitting, the proc 
ess of breaking a pixel into its component parts and 
classifying the fractions. Pixel splitting methods are 
based on the premise that the gray tone represented by 
a digital number of a pixel is proportional to the 
gray tones of the constituent classes. Four statisti 
cal procedures for pixel splitting have been found in 
the literature! weighted averaging (Marsh et al. 1980), 
linear regression (Nalepka et al. 1972, Richardson & 
Weigand 1977), maximum likelihood classification 
(Chittineni 1981, Horwitz et al. 1971), and linear 
discriminant analysis (Marsh et al. 1980). The first 
three of these procedures have been or could easily be 
used in the three-class case, the usual condition for 
pixels containing narrow woody features. 
For a number of reasons, the methods already devel 
oped seemed inappropriate for this project. Treating 
mixed pixels as whole entities was not appropriate be 
cause linear woody features are seldom the dominant 
constituent class. The variances of the major classes 
in the subscene are significantly different; the var 
iance of woody vegetation is significantly larger than 
the variance of any other class. Therefore, it seemed 
unsuitable to use either weighted averaging which does 
not take variance into consideration or linear dis 
criminant analysis which requires the variances of all 
classes to be equal. The linear regression method was 
eliminated since development of the model requires 
specific information regarding the proportions of com 
ponent parts within mixed pixels. The small sizes of 
the features of interest and the geometric distortions 
in the image make collection of this specific informa 
tion difficult. Finally, the maximum likelihood method 
seemed inappropriate because it is the most computa 
tionally demanding of the known methods. Since various 
postprocessing algorithms are planned, it seemed de 
sirable to conserve computer time. 
Figure 2. Three parts of the 13 x 3 window. 
4 NEW METHOD FOR CLASSIFYING SUBPIXEL WOODY FEATURES 
The classification method developed for this project 
discriminates selected subdivisions of feature space. 
Figure 1 illustrates the subdivisions of interest in 
a hypothetical feature space which was constructed us 
ing two spectral channels and five pixels from each of 
three classes. If the triangles and squares represent 
two crops or any two land covers present in adjacent 
fields, pixels containing the boundary between the 
fields are expected to fall between the line joining 
points B and C and the line joining points E and F. 
If, however, the boundary between the fields includes 
a linear woody feature and if the circles represent 
woody vegetation, pixels containing this boundary are 
more likely to fall within the polygon circumscribed 
by points A, B, G, and D. Locating a pixel in one of 
these two zones is the basis for classifying it in ei 
ther the class of normal boundaries or the class of 
small woody features. 
Nine major classes present in the study site were 
plotted in the two-dimensional feature spaces whose 
axes are pairs of the multispectral bands. The pan 
chromatic data was not used. Visual inspection of the 
plots revealed that bands 1 and 2 are highly correlat 
ed and that the greatest separation between classes 
occurs when bands 2 and 3 are used. Therefore, only 
bands 2 and 3 were included in subsequent processing; 
band 1 was eliminated due to its redundancy. 
A training set for woody vegetation, consisting of 
80 pixels, was selected from a large patch of decidu 
ous woodland in the subscene and was used in all fea 
ture-space calculations. 
A 13 x 3 window was employed to locate its central 
pixels in the two-channel feature space. As shown in 
Figure 2, the window was divided into three parts: two 
training sets for the adjacent fields and a central 
region expected to contain mixed pixels. These two 
training sets and the training set for woody vegeta 
tion were used to construct a feature space for each 
window. The central pixels in the window were then 
Figure 3* Fea-t 
classified by 
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Figures 3-5. I 
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5 RESULTS AND : 
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initial testing 
greatly vary ir 
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All of the wooc 
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classification. 
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visual inspects 
The classifie 
correctly class 
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improve classif 
Figures 3-5 1 
training sets m 
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When adjacent f; 
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woody features ; 
woodland cluster 
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Figure 5» pixel: 
less likely to 1 
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only occurred or 
Before this me