Full text: Remote sensing for resources development and environmental management (Volume 1)

Ln a hypothetical 
training set 
x 3 window. 
ped for this project 
ons of feature space, 
sions of interest in 
ch was constructed us- 
ve pixels from each of 
and squares represent 
present in adjacent 
undary between the 
sen the line joining 
ing points E and F. 
i the fields includes 
e circles represent 
Lng this boundary are 
slygon circumscribed 
lg a pixel in one of 
classifying it in ei- 
Les or the class of 
the study site were 
sature spaces whose 
?al bands. The pan- 
ial inspection of the 
l are highly correlat- 
.on between classes 
sd. Therefore, only 
ibsequent processing; 
ition, consisting of 
trge patch of decidu- 
was used in all fea- 
> locate its central 
s space. As shown in 
into three parts: two 
.elds and a central 
pixels. These two 
¡t for woody vegeta- 
.ture space for each 
i window were then 
L = 79 S = 149 
Figure 3. Feature space created from window data. 
classified by their locations in this space. 
■ Examples of this classification method are shown in 
Figures 3-5’ In these figures, squares represent woody 
vegetation, triangles pointing downward and triangles 
pointing upward represent the two training sets de 
rived from the window, and circles represent the cen 
tral pixels in the window. The circle with the dot in 
dicates the pixel at the exact centre of the window. 
L and S refer to the line and sample of the pixel in 
the centre. In each example, the circles to the left 
of or below the line are placed in the class of small 
woody features. 5 
Twenty subpixel woody features and two simple field 
boundaries were selected from the study site for the 
initial testing of the method. The woody features 
greatly vary in width and length and include hedge 
rows, a hedge with one large tree, single rows of 
trees, and strips or patches two or more trees wide. 
All of the woody features selected were found to be 
incorrectly detected by a standard nearest neighbor 
classification. Most of the features were also unable 
to be either detected or adequately interpreted by 
visual inspection of a colour-composite image. 
The classification method developed in this project 
correctly classified all subpixel woody features larg 
er than hedgerows, properly discriminated the simple 
field boundaries, and identified about 20 percent of 
the hedges. Further testing is in progress. 
Use of the panchromatic band will be incorporated 
into future classification schemes and is expected to 
improve classification accuracy for hedgerows. 
Figures 3-5 illustrate the three possible ways the 
training sets may be situated in feature space. The 
roughly triangular shape, suggested by the training 
sets in Figure 3, is the most typical arrangement. 
When adjacent farm fields contain the same land cover, 
the clusters of their training sets overlap as in Fig 
ure 4. In both of these cases, pixels containing small 
woody features are likely to be pulled toward the 
woodland cluster. However, when the three training 
clusters are positioned along a straight line, as in 
Figure 5» pixels containing small woody features are 
less likely to be pulled past the middle cluster into 
the zone designating woody features. This last case 
only occurred once in the 22 features tested. 
Before this method can be completely operational, a 
L = 134 S = 95 
L = 57 S = 171 
70 10 *0 ICO 110 120 130 1*0 150 140 170 
Figure 5* Feature space created from window data. 
few practical problems must be addressed. The most 
important problem is determining an optimal size for 
the window. The 13 x 3 window is frequently too big 
to fit into the small comers of farm fields. This 
inability to fit also contributes to the creation of 
anomalies in the training sets. Four anomalous train 
ing pixels are shown in Figure 6; the correct posi 
tion of the division line in this feature space is 
also shown. A smaller window, like a 9 x 1 window, 
would fit into smaller fields and would create fewer 
anomalies. A smaller window would also be more effi 
cient since it would less frequently use the same 
pixels in its calculations. 
The automated use of this method could provide an 
effective means of mapping and monitoring the pres 
ence of subpixel woody features in satellite imagery. 
Also, the use of this method could provide the basis 
for determining specific quantitative information of 
use to ecologists and resource managers.

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