Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/6892| DC Field | Value | Language |
|---|---|---|
| dc.contributor.authorall | Chini, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia | en |
| dc.contributor.authorall | Pacifici, F.; University of Tor Vergata | en |
| dc.contributor.authorall | Emery, W.; University of Colorado | en |
| dc.contributor.authorall | Pierdicca, N.; Sapienza University of Rome | en |
| dc.contributor.authorall | Del Frate, F.; University of Tor Vergata | en |
| dc.date.accessioned | 2011-01-24T12:14:30Z | en |
| dc.date.available | 2011-01-24T12:14:30Z | en |
| dc.date.issued | 2008-06 | en |
| dc.identifier.uri | http://hdl.handle.net/2122/6892 | en |
| dc.description.abstract | Parametric 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. | en |
| dc.language.iso | English | en |
| dc.publisher.name | IEEE | en |
| dc.relation.ispartof | Transaction on Geosciences and Remote Sensing | en |
| dc.relation.ispartofseries | 6/46(2008) | en |
| dc.subject | Maximum likelihood (ML) | en |
| dc.subject | neural networks (NNs) | en |
| dc.subject | urban change detection | en |
| dc.subject | very high resolution (VHR) satellite images | en |
| dc.title | Comparing statistical and neural network methods applied to very high resolution satellite images showing changes in man-made structures at Rocky Flats | en |
| dc.type | article | en |
| dc.description.status | Published | en |
| dc.type.QualityControl | Peer-reviewed | en |
| dc.description.pagenumber | 1812-1821 | en |
| dc.subject.INGV | 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation | en |
| dc.identifier.doi | 10.1109/TGRS.2008.916223 | en |
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Ling, “Sampling for the thematic map accuracy testing,” Photogramm. Eng. Remote Sens., vol. 48, pp. 131–137, 1982. [22] G. F. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55–63, Jan. 1968. [23] D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ: Wiley, 2003. [24] J. Friedman, “Regularized discriminant analysis,” J. Amer. Stat. Assoc., vol. 84, no. 405, pp. 165–175, Mar. 1989. [25] J. Hoffbeck and D. Landgrebe, “Covariance matrix estimation and classification with limited training data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 7, pp. 763–767, Jul. 1996. [26] M. D. Richard and R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Comput., vol. 3, no. 4, pp. 461–483, 1991. [27] H. Gish, “A probabilistic approach to the understanding and training of a neural network classifier,” in Proc. IEEE Int. Conf. Acoust., Signal, Speech Process., Albuquerque, NM, 1990, vol. 3, pp. 1361–1364. [28] J. D. Paola and R. A. Schowengerdt, “A detailed comparison of backpropagation neural network and maximum-likelihood classifier for urban land use classification,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 4, pp. 981–996, Jul. 1995. | en |
| dc.description.obiettivoSpecifico | 1.10. TTC - Telerilevamento | en |
| dc.description.journalType | JCR Journal | en |
| dc.description.fulltext | reserved | en |
| dc.contributor.author | Chini, M. | en |
| dc.contributor.author | Pacifici, F. | en |
| dc.contributor.author | Emery, W. | en |
| dc.contributor.author | Pierdicca, N. | en |
| dc.contributor.author | Del Frate, F. | en |
| dc.contributor.department | University of Tor Vergata | en |
| dc.contributor.department | University of Colorado | en |
| dc.contributor.department | Sapienza University of Rome | en |
| dc.contributor.department | University of Tor Vergata | en |
| item.openairetype | article | - |
| item.cerifentitytype | Publications | - |
| item.languageiso639-1 | en | - |
| item.grantfulltext | reserved | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| item.fulltext | With Fulltext | - |
| crisitem.author.dept | Luxembourg Institute of Science and Technology (LIST) | - |
| crisitem.author.dept | Digital Globe, Research and Development, Longmotn, CO, USA | - |
| crisitem.author.dept | University of Colorado, Boulder (CO), USA | - |
| crisitem.author.dept | Sapienza Università di Roma | - |
| crisitem.classification.parent | 05. General | - |
| Appears in Collections: | Article published / in press | |
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| 2008_TGRS_Chini_et_al.pdf | 3.11 MB | Adobe PDF |
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