Mr. Marco Giardino Profile

Marco Giardino

at Univ degli Studi di Torino

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Proceedings Article | 22 October 2004 Paper

MIVIS image classification for the geomorphological characterization of large slope instabilities in Aosta Valley, Italian Northwestern Alps

Marco Giardino, Enrico Borgogno Mondino, Luigi Perotti

Proceedings Volume 5574, (2004) https://doi.org/10.1117/12.565509

KEYWORDS: Sensors, Image classification, Neural networks, Landslides, Remote sensing, Infrared radiation, Distortion, Image processing, 3D modeling, Error analysis

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Natural hazards monitoring and analysis have come to be very important sectors in environmental management. In the last decades, natural slope dynamics of high mountain relief in Aosta Valley has been analyzed by means of detailed geological and geomorphological field surveys. For a long time, remote sensing techniques have been an effective tool within inaccessible locations thanks to their wide analysis. This article intends to present some results coming from investigations on high elevation alpine environments, based on the high resolution hyperspectral airborne sensor MIVIS images. Considering the great problem due to the high geometric distortion of such data, related both to the sudden relief displacement and to the intrinsic whiskbroom recording system, we are willing to show an experimental neural network approach for geometric correction which is a very important item for measurement instances. To enhance geomorphological characterization and hazards studies on large slope instabilities we have successively applied a neural network LVQ 2 (Modified Learning Vector Quantization) self-developed classifier exploiting spectral information coming from images. The application of the method to the Val Veny- Val Ferret area (Mont Blanc zone) allowed a better definition of the active deformational features connected to flexural toppling, deep creep and superficial fracturing along double ridges and steep slopes, eventually preparing for collapse along high rock walls. Hazard scenarios for deep seated gravitational slope deformations of the Upper Aosta Valleys can be more precisely outlined exploiting MIVIS images hyperspectral contents, but first the geometric correction problem has to be preventively solved.

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