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Title
Proceedings of the Symposium on Global and Environmental Monitoring

were identified through a field study in the time of satel
lite snap shoting. This study and maps formed the ground
truth data base for our field definition and class identifica
tion. The digital computations were performed using inter
active image analysis system developed by the author. The
mean vector and covariance matrix of each training field
were calculated to develop the spectral signature represen
tative of land-cover classes. The objective of the analysis,
was to discriminate among following 15 agricultural classes:
water, red clover, white clover, wood, wheat, maize, mix
ture, millet, grass, harvested rapeseed, rapessed, sugarbeet,
wheat II, clover and residential area. The resubstitution
estimations of the probability of correct classification are
summarised in the table.
performance. The overall performance of contextual clas
sifier is better in tested example than the non-context per-
point Bayesian one. Similar improvement is seen for all
classes to be searched. This result was reached with the
smallest possible context window of eight surrounding pix
els. The optimal size of window depends on local condi
tions ( average field area ) and on the used image resolu
tion. Computational time of presented algorithm was in
tested example approximately five times longer than for
the standard Bayesian classification one. Further work is
still needed to optimize developed software to increase clas
sification speed.
REFERENCES
Peterka,V.,19Sl.Bayesian approach to system identification.
In. Trends and Progress in System Identification. Ed. P.
Eykhoff , Pergamon Press, Oxford.
Tubbs,J.D.,1980. Effect of Autocorrelated Observation on
Confidence Sets Based upon Chi-Square Statistics. IEEE
Trans. Syst. Man. and Cybern., vol. SMC'-10(4): 177-180.
class
Contextual
Bayesian c.
Bayesian
classifier
1
1
1
2
0.8
0.72
3
0.9
0.82
4
0.95
0.S1
5
0.98
0.93
6
1
1
7
0.92
0.79
8
1
0.93
9
0.77
0.56
10
0.97
0.97
11
1
0.89
12
0.99
0.91
13
0.85
0.6
14
0.92
0.88
15
0.95
0.68
P
0.93
0.85
We have chosen the autoregressive model of order one (N =
1) and the smallest possible thematic map window of nine
pixels (n = 9). In such a case, the contextual classifier
needs h(*) — 2010, h( + ) = 1479 operations, while the
conventional approach only /?.(*) = 525, h{ + ) = 435 ones.
6 CONCLUSION
The tested example shows improvement in classification
342