ray EEE EEE
-parametric
with M =0
tion regions
figure 9 (b).
rage and the
nd temporal
yy using the
» parametric
idy, we will
ng to degree
ed based on
See’ | NSA] FT] ep |
Yosuke Ito
(a) LVQ with M — 6 (b) ML
Figure 9: Classification images using C»
ACKNOWLEDGMENTS
The SAR data used in this study were provided by National Space Development Agency of Japan (NASDA). The authors
would like to thank JPL, NASA, Dr Shimada at NASDA and Dr Hanaizumi at Hosei University, Japan for the inter-
ferometric SAR software. The image processing facilities are supported by the University of London Inter-Collegiate
Research Services.
REFERENCES
Fujisawa, S., Rosen, P. A., 1998. Crustal deformation measurements using repeat-pass JERS 1 synthetic aperture radar
interferometry near the Izu Peninsula, Japan, Journal of Geophysical Research, 103(B2), pp. 2411-2426.
Ito, Y., Omatu, S., 1997. Category classification method using a self-organizing neural network, International Journal of
Remote Sensing, 18(4), pp. 829-845.
Kohonen, T., 1997. Self-Organizing Maps. Springer, Berlin, pp. 203-217.
Lee, H., Liu, J. G., 1999. Spatial decorrelation due to topography in the interferometric SAR coherence imagery. In:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS’99) Proceedings, Hamburg, Germany, Vol. 1,
pp. 485-487.
Richards, J. A., 1993. Remote Sensing Digital Image Analysis. Springer-Verlag, Berlin, pp. 271—275.
Yonezawa, C., Takeuchi, S., 1999. Detection of urban damage using interferometric SAR decorrelation. In: IEEE Interna-
tional Geoscience and Remote Sensing Symposium (IGARSS'99) Proceedings, Hamburg, Germany, Vol. 2, pp. 925-927.
Zebker, H. A., Villasenor, J., 1992. Decorrelation in interferometric radar echoes. IEEE Transactions on Geoscience and
Remote Sensing, 30(5), pp. 950—959.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part Bl. Amsterdam 2000. 163