DSpace - Tor Vergata: Item 2108/568

Please use this identifier to cite or link to this item: http://hdl.handle.net/2108/568

Title:	High resolution urban monitoring using neural network and transform algorithms
Authors:	Del Frate, Fabio Emery, William J. Solimini, Chiara
Keywords:	change detection neural networks image information mining (IMM) high resolution FFT
Issue Date:	5-Aug-2008
Abstract:	The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover from space. Satellite observations are carried out regularly and continuously and provide a great deal of information on land cover over large areas. High spatial resolution imagery makes it possible to overcome the “mixed-pixel” problem inherent in more moderate resolution satellite sensors. At the same time, high-resolution images present a new challenge over other satellite systems since a relatively large amount of data must be analyzed, processed, and classified in order to characterize land cover features and to produce classification maps. Actually, in spite of the great potential of remote sensing as a source of information on land cover and the long history of research devoted to the extraction of land cover information from remotely sensed imagery, many problems have been encountered, and the accuracy of land cover maps derived from remotely sensed image...
URI:	http://hdl.handle.net/2108/568
Appears in Collections:	Tesi di dottorato in ingegneria

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