as post-
xels were
(1)
between
oÜnging to
yntextual
nizes the
s of the
ghboring
ld (MRF)
a'priori
ilt of the
ld (MRF)
ities are
classified
s (Hord,
'omputed
jr matrix
esent the
or class 1
ata class
sses. The
correctly
presents
a classes
cy values
id, 1994):
‘the total
the total
iding the
ach class
] for that
robability
reference
ling pixel
ding the
ach class
issified to
robability
epresents
sification
ween the
data and
greement
random
classification. If KHAT is close to one it indicates
that the classification result is better than a
random classification.
The error matrix of LVQ-classification is represented in
Table 3. The overall accuracy was 86.4% and kappa 0.83.
The producer’s and user’s accuracies are represented in
Table 5. Contextual classification increased the overall
accuracy a bit, it was 88.596 (kappa 0.85) for CI-
classifier and 87.3% (kappa 0.84) for M1-classifier. The
best classifier was M2 when transitional probabilities
were estimated using 7 by 7 pixel window. In that case
the overall accuracy was 89.8% and kappa 0.87.
Corresponding error matrix is represented in Table 4
and the producer’s and user’s accuracies in Table 5.
Water and agricultural field were classified very well,
producer’s and user’s accuracies were over 99% and 94%,
respectively. Some pixels belonging to agricultural field
were classified as forest with low stem volume. Urban
area and mire were classified moderately well,
producer’s and user’s accuracies were over 77% and 78%,
respectively. These classes were mixed mainly with
forest classes. Forest classes were classified from very
poor to moderate, they were mainly mixed with each
other or mire and urban area. Very poor accuracy of
forest, stem volume 100 - 200 m?/ha was probably due to
small sample size used in the training of the classifier.
Contextual classification increased the classification
accuracies except for forest, stem volume 100 - 200
m/ha.
8. CONCLUSIONS
Generally the mean backscatter of classes is higher
during summer than winter and the temporal variations
of the mean backscatter are higher during summer and
autumn than during winter. Backscatter from forest
classes is lowest when ground is frozen and covered with
thin layer of snow. Accumulation of new snow increases
backscatter and aging of snow decreases it. Variations of
forest classes increase according to the increase in stem
volume. Also differences in backscatter between summer
and winter increase with the stem volume. Differences
in backscatter of mire and forest classes are largest
during summer, otherwise backscatter behaves quite
similar. The backscatter from urban area does not vary
much, it is almost undependent from season.
The mean class separabilities are largest for Jers,
summer and late autumn ERS-1, snow-free and unfrozen
Radarsat and frozen Radarsat images. The separabilities
are smallest for winter and very rainy ERS-1 images, so
ground freezing and heavy rain decrease the suitability
of ERS-1 images for land-use classification. Median
filtering increases the class separability.
The results of classifications were satisfactory. The
overall accuracy was 86.496 for LVQ-classification and
89.8% for the best contextual classification. Water and
agricultural field were classified very well and urban
area and mire morerately well. Forest classes are the
most problematic, they mix with each other, mire and in
some cases with urban area.
ACKNOWLEDGMENTS
This study is part of European Union funded EUFORA-
project and conducted under the supervision of Helsinki
University of Technology, Laboratory of Space
Technology. Authors wish to thank Dr.Tech. Jouni
Pulliainen for help and comments.
REFERENCES
Devivjer P., Kittler J., 1982. Pattern Recognition - A
Statistical Approach. Prentice-Hall, 1982.
Hord M., 1986. Remote Sensing - Methods and
Applications. John Wiley & Sons, 1986.
Jain A., Dubes R., 1988. Algorithms for Clustering Data.
Prentice-Hall, 1988.
Kohonen T., 1990. The Self-Organizing Map. Proceedings
of the IEEE, Vol. 78, no. 9, September 1990, pp. 1464-
1480.
Lillesand T., Kiefer R., 1994. Remote Sensing and Image
Interpretation. John Wiley & Sons 1994.
Pulliainen J., Mikkel4 P., Hallikainen M., Ikonen J-P.,
1996. Seasonal Dynamics of C-Band Backscatter of
Boreal Forests with Applications to Biomass and Soil
Moisture Estimation. IEEE Transactions on Geoscience
and Remote Sensing, Vol. 34, no. 3, May 1996, pp. 758-
769.
Richards J., 1993. Remote Sensing Digital Image
Analysis. 2nd ed. Springer-Verlag, 1993.
Rauste Y., 1989. Methods for Analyzing SAR images.
Tech. Res. Ctr. Finland, Lab Instrument Technol.
Espoo, Finland, Rep. 612, 1989.
Tomppo E., 1997. Finnish National Forest Inventory.
Proceedings of the Finnish - Russian seminar on remote
sensing in Helsinki, 29. Aug. - 1. Sep. 1994. Reports of
the Finnish Geodetic Institute, 97:2.
Tórmà M, 1997. Land-Use Classification Using SAR-
Images. Photogrammetric Journal of Finland, Vol. 15,
no. 2, 1997, pp. 31-47.
Vuorela A., 1997. Satellite Image Based Land Cover and
Forest Classification of Finland. Proceedings of the
Finnish - Russian seminar on remote sensing in
Helsinki, 29. Aug. - 1. Sep. 1994. Reports of the Finnish
Geodetic Institute, 97:2.
Intemational Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 571