Detecting landslides and monitoring their activity is of great relevance
for disaster prevention, preparedness and mitigation in hilly areas.
To this end, we have investigated and developed a variety of
change detection techniques that have been applied
to multi-temporal digital aerial photographs.
This works involves various strategies for image differencing and thresholding,
shape analysis, texture based classification and genetic programming.
More details are given in:
P.L. Rosin, J. Hervas,
"Remote Sensing Image Thresholding Methods for Determining Landslide Activity",
International Journal of Remote Sensing, vol. 26, no. 6, pp. 1075-1092, 2005.
J. Hervas , J.I. Barredo, P.L. Rosin, A. Pasuto, F. Mantovani and S. Silvano,
"Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy",
Geomorphology, vol. 54, pp. 63-75, 2003.
P. Rosin and J. Hervas, Image thresholding for landslide detection by Genetic Programming. In: L. Bruzzone and P.C.
Smits (eds.), Analysis of multi-temporal remote sensing images. World Scientific, 2002, pp. 65-72.
J. Hervas and P.L. Rosin, "Landslide mapping by textural analysis of ATM data", Proc. Eleventh Thematic Conference on
Applied Geologic Remote Sensing, Las Vegas, USA, vol. 2, pp. 394-402, 1996.
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