theoretical drawback is that Maximum Likelihood assumes 
the classes being normally distributed. 
One Neural Network method that is a non-parametric clas- 
sifier is Learning Vector Quantization (LVQ) by Kohonen 
[Hertz, 1991, Kohonen, 1989, Kohonen, 1995]. It looks very 
promising because of the efficient training process and its 
capability to learn non-normally distributed classes. The pur- 
pose of the training process of LVQ is to find a “codebook” 
which is a quantization of the training data. This codebook 
can be used to classify the entire image by performing a near- 
est neighbour labeling process. 
5.2 True error rate approximation 
Having no extended ground truth the performance criteria for 
training is true error rate approximation. 
In a first step we divided all training pixels randomly into 
reference and testing sets. The sets are given in table 2. 10- 
Table 2: Ground truth 
  
  
forest type | stand age | stand density 
all 3145 4598 4631 
reference 2661 4088 4142 
test 484 510 489 
  
  
  
  
  
  
fold cross validation was used to get approximations of the 
true error rates of the classifiers. À further statistical mean 
of estimating the performance was to carry out one design 
and test step to obtain the confusion matrices. 
5.3 Verification 
Two experts from the Carinthian government and the most 
experienced collegue from the institute verified the results vi- 
sually. This step was very important because the quality of 
the ground truth was not known. Furthermore, our experi- 
ence with LVQ was limited at that point of time. 
6 MOSAICKING 
Due to the spatial distribution of the training areas all over 
of Carinthia a seperate training for all satellite scenes was 
not possible. For the scenes covering the eastern and west- 
ern part of Carinthia respectively, the ground truth was not 
covering all classes sufficiently. Thus, first the main scene, 
which covers most part of Carinthia, was trained with the 
ground truth and classified. In order to establish a classifica- 
tion mosaic of all satellite scenes, the classification results of 
the main scene within the scene overlay were used as train- 
ing areas for the classification of the edge scenes. These had 
to be classified separately and combined to a classification 
mosaic afterwards. 
Table 3: Statistical results 
Classification True error rate 
Forest type 69.51% + 0.78% 
Stand age 65.29% + 0.96% 
Stand density | 83.57% + 0.65% 
  
  
  
  
  
  
Furthermore, some small areas covered by clouds had to be 
replaced by classification results of cloud free scenes. Wher- 
ever possible, the edge scenes were used for this purpose. 
However, for some parts the central scene had to be brought 
604 
in using the image not empoloyed in the original classification 
of the respective forest parameter. 
7 RESULTS 
The final approved classifications were done with Maximum 
Likelihood for forest type and stand age, and LVQ for stand 
density. While the statistical results were better for LVQ in 
all three classifications, the experts found problems With the 
LVQ results. Small classes tended to be underrepresented, the 
overall result was too smooth in appearance. While training 
with LVQ problems were encountered with repeatability of 
the training results with identical sample sets. Furthermore, 
we are suspicious that we did not obtain optimal results with 
LVQ. As Song and Lee [Song, 1996] point out in their very 
recent paper, mean problems of LVQ are: 
1. good initial values for the codebook 
2. no garantee for optimal codebook 
3. optimal stopping point. 
The CV results of the approved classifications are given in ta- 
ble 3, the confusion matrices in table 4.to 6. The mosaicking 
resulted in complete classified images where the cutting line 
remained invisible. 
Table 4: Forest type: confusion matrix 
(a) | (b) | (c) | (d) | « classified as 
77:40: 112 5 | (a): deciduous 
31-|321.4. 20 5 | (b): mixed deciduous 
8 12 | 56 | 27 | (c): mixed coiferous 
7 9 | 12 | 172 | (d): coniferous 
  
  
  
  
  
  
  
  
  
Table 5: Stand age: confusion matrix 
  
  
  
  
(a) |l (b) |- (c) | <= classified as 
103 | 45 | 18 | (a): young stands 
58 | 183 | 36 | (b): mature stands 
11 15 | 41 | (c): old stands 
  
  
  
  
Table 6: Stand density: confusion matrix 
(a) | (b) | « classified as 
54 | 40 | (a): 0 — 6096 
43 | 352 | (b): » 6096 
  
  
  
  
  
  
  
8 CONCLUSION 
As to chosing the best classifier it is very important to 
examine the confusion matrix and - additionally - to verify 
the results to obtain the desired outcome. This process was 
carried out together with the client which may not be practi- 
cal in general, however, this approach was the only possibility 
to provide the client with adequate results due to the prob- 
lems mentioned. 
Neural Network classifiers do have tempting features but also 
unexpected drawbacks. We do not recommend to experiment 
with new classifiers when there is existing experience, because 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996