Change detection in urban areas with QuickBird imagery and Neural Networks algorithms

F. Del Frate, G. Schiavon, C. Solimini

Abstract

In this paper we report on some results obtained by applying the high resolution imagery of QuickBird for the analysis of the selected urban test area: the Tor Vergata University campus, located in Italy, South-East of Rome, whose extention is of about 600 ha. Initially, a pixel-based classification algorithm based on neural networks has been implemented to automatically discriminate between some identified type of surfaces, in particular bare soils, vegetated areas, buildings and asphalted surfaces. Change detection maps have been successively produced by applying the classification procedure to two different images taken at different dates.

INTERNATIONAL SOCIETY OF PHOTOGRAMMETRY AND REMOTE SENSING

Proceedings of the ISPRS joint conference
3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas (URBAN 2005)

5th International Symposium Remote Sensing of Urban Areas (URS 2005)

Tempe, AZ, USA, March 14-16 2005

Editors: M. Moeller, E. Wentz

International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
VOLUME XXXVI, PART 8/W27
ISSN 1682-1777

© 2005 the authors

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