In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
311 
Figure 1. Stand (black) and segment (white) boundaries in aerial 
photograph (near-infrared channel). Copyright for 
the aerial photograph owned by Blom Kartta Oy 
Altogether, 9 156 segments were used. The segmentation 
algorithm was same as in the study of Hyvonen et al. (2005). 
Altogether, 75 features both at the stand and segment levels 
were extracted, of which 36 were spectral features extracted 
from the original photographs and 39 were features from the 
adjustment of and regression between the photographs. These 
features were used in the change detection analysis. 
Two different methods for change detection were tested; linear 
stepwise discriminant analysis and the non-linear ^-nearest 
neighbour (&-NN) method. The classification functions of the 
discriminant analysis and the weights of the variables of the 
similarity distance functions, as well as the number of the 
neighbours of &-NN method, were estimated for the three 
change classes in the training data. The multi-objective 
optimisation, combined with the k-NN method, was used to 
choose the weights of the variables of the similarity distance 
functions, as well as the number of the neighbours of k-NN 
method (Haara, 2000). The nonlinear programming algorithm 
presented by Hooke and Jeeves (1961) was used to find the 
combination of decision variables that minimised the objective 
function. The optimised objective variable was the sum of the 
Kappa value and the percentage of correctly classified 
moderate-change stands. Both methods, i.e. the estimated 
classification functions of the discriminant analysis and £-NN 
method with the training data as the reference data, were then 
applied to the test data. The accuracy of the classification was 
studied at the stand level. 
The accuracy of the classification results was evaluated by 
means of confusion matrices (Campbell, 1987) and the overall 
(OA), producer’s (PA) and user’s (UA) accuracies were 
calculated (Congalton et al., 1983; Story & Congalton, 1986). 
The OA can give too optimistic results if the proportion of one 
class is high (Campbell, 1987) and for this reason the k (kappa) 
coefficient was calculated (Congalton & Mead, 1983). 
3. RESULTS 
When stands were used as classification units, the overall 
accuracy of the discriminant analysis was 91.1%, the Kappa- 
value 0.74, the omission error 16.1% and the commission error 
7.2% (Tables 2 and 6). The main interest concerned thinned 
stands, of which 90.7% were found. When segments were used 
as classification units and the results were studied at the stand 
level, the overall accuracy of the discriminant analysis was 
90.1%, the Kappa-value 0.71, the omission error 13.8% and the 
commission error 8.8% (Tables 3 and 6 ). Of the thinned stands 
89.7% were found. 
Wfren using the k-NN method, the overall accuracy was 91.5% 
and the Kappa value was 0.72 (Tables 4 and 6). The proportion 
of correctly classified thinned stands was 82.5%. When the 
classification was done at the segment level, the stand wise 
overall accuracy was 86.6% and the Kappa value 0.62 (Tables 5 
and 6). In this case, 86.6% of the thinned stands were found. 
After rectification (at the image level) 12 stands had been 
moved more than 5 pixels in every channel in the direction of x 
and 18 stands in direction of y in the final adjustment between 
the aerial photographs. Only one stand of these was in the No 
change class. 
Observation 
No-change 
Estimated 
Mod.-change 
Con.-change 
No-change 
874 
68 
0 
Mod.-change 
35 
139 
0 
Con.-change 
0 
0 
44 
Table 2. Classification results of the discriminant analysis at the 
stand level, stands as a priori classification units. 
Table 5. Classification results of the &-NN method at the stand 
level, segments as a priori classification units. 
Observation 
No-change 
Estimated 
Mod.-change 
Con.-change 
No-change 
859 
83 
0 
Mod.-change 
30 
144 
0 
Con.-change 
0 
2 
42 
Table 3. Classification results of the discriminanat analysis at 
the stand level, segments as a priori classification 
units. 
Observation 
No-change 
Estimated 
Mod.-change 
Con.-change 
No-change 
900 
40 
2 
Mod.-change 
52 
121 
1 
Con.-change 
1 
3 
40 
Table 4. Classification results of the £-NN method at the stand 
level, stands as a priori classification units. 
Observation 
No-change 
Estimated 
Mod.-change 
Con.-change 
No-change 
834 
107 
1 
Mod.-change 
41 
130 
3 
Con.-change 
0 
3 
41