Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/6892
AuthorsChini, M.* 
Pacifici, F.* 
Emery, W.* 
Pierdicca, N.* 
Del Frate, F.* 
TitleComparing statistical and neural network methods applied to very high resolution satellite images showing changes in man-made structures at Rocky Flats
Issue DateJun-2008
Series/Report no.6/46(2008)
DOI10.1109/TGRS.2008.916223
URIhttp://hdl.handle.net/2122/6892
KeywordsMaximum likelihood (ML)
neural networks (NNs)
urban change detection
very high resolution (VHR) satellite images
Subject Classification05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation 
AbstractParametric and nonparametric approaches to evaluate land-cover change detection using very high resolution (VHR) satellite imagery are applied to the analysis of the demolition of the Rocky Flats nuclear weapons facility located near Denver, CO. Both maximum-likelihood and neural network classifiers are used to validate a new parallel architecture which improves the accuracy when applied to VHR satellite imagery for the study of land-cover change between sequential satellite acquisitions. An enhancement of about 14% was found between the single-step classification and the new parallel architecture, confirming the advantage and the robust improvement obtained with this architecture regardless of the classification algorithm used. In this paper, we demonstrate and document the demolition and removal of hundreds of buildings taken down to bare soil between 2003 and 2005 at the Rocky Flats site.
Appears in Collections:Papers Published / Papers in press

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