97 
Figure 2: a) Boundary starting position 2; b) Latent variable p(B’), 
iteration 1; c) Distance transform (DT) of class label boundaries, 
iteration 1. This defines the topographic surface used for boundary 
movement; d) Latent variable p(B’), iteration 2; e) Latent variable 
p(B’), iteration 5; f) Distance transform of class label boundaries, 
iteration 5. This defines the topographic surface used for 
boundary, movement; g) Boundary position, iteration 5; h) Class 
label image out; i) Class label image without use of the model. 
4 Examination of model properties using a real example 
Experiments were performed with data from the south west of 
Western Australia shown in figure 3 below. The region includes 
the area covered by the Landsat scenes of Pemberton(l 12/84) and 
Mt Barker(l 11/84), and is identified as the 1:1 000 000 map sheet 
si50. 
Figure 3. Region covered by the MLC classifier. Area shown is 
approx 400 by 200km. 
The aim is to provide improved classification and boundaries for 
forest plantations (Softwood and Harwood), and for native 
eucalypt forests (Karri, Jarrah). Analysis of Landsat TM and areas 
of known plantation and regeneration types demonstrate that • 
• The ability to distinguish the different types based on 
their spectra alone varies as they grow 
• Mature softwood and 4-6 year old karri regrowth show 
the best spectral separation from the remaining classes 
• Jarrah and hardwood had the least separation 
Mapped versus true class label pixel counts are given in table 1 
below. These figures were used to generate table 2, P(True Class 
Labels I Mapped Labels) , and table 3, P(Mapped Label I True 
Class Label). 
True Class Labels 
Mapp 
ed 
SW 
K 
HW 
J 
Total 
Swl 
3876 
178 
213 
292 
4559 
Sw 2 
1216 
893 
379 
86 
2574 
K1 
15 
5485 
207 
588 
6295 
K 2 
242 
19559 
620 
1882 
22303 
HW 
176 
1397 
6973 
2223 
10769 
J 
252 
1458 
1572 
19833 
23115 
B 
1020 
1047 
4632 
1165 
7864 
Total 
6797 
30017 
14596 
26069 
77479 
Table 1 - Number of pixels tabulated by mapped and true class. 
P(True Class Labels 1 Mapped) 
Mapped 
SW 
K 
HW 
J 
Total 
Swl 
0.85 
0.04 
0.05 
0.06 
1.0 
Sw2 
0.472 
0.347 
0.147 
0.03 
1.0 
K1 
0.002 
0.87 
0.03 
0.09 
1.0 
K2 
0.01 
0.877 
0.028 
0.084 
1.0 
HW 
0.016 
0.13 
0.648 
0.206 
1.0 
J 
0.01 
0.06 
0.068 
0.858 
1.0 
B 
0.07 
0.32 
0.18 
0.43 
1.0 
Table 2 - Probability of true class label given map label 
P(Mapped 1 True Class Labels) 
Mapped 
SW 
K 
Hw 
Jarrah 
Swl 
0.57 
0.01 
0.01 
0.01 
Sw2 
0.18 
0.03 
0.03 
0.00 
K1 
0.00 
0.18 
0.01 
0.02 
K2 
0.04 
0.65 
0.04 
0.07 
Hw 
0.03 
0.05 
0.48 
0.09 
J 
0.03 
0.05 
0.11 
0.76 
B 
0.15 
0.03 
0.32 
0.05 
Total 
1.0 
1.0 
1.0 
1.0 
Table 3 Probability of mapped class label given true label. 
The accuracy of the classification was estimated using 268 sites 
having labels from the set {Jarrah, Karri, HardWood, SoftWood}. 
The sites are independent from the sites used to train the classifier.