Concurrent self-organizing maps for supervised/unsupervised change detection in remote sensing images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 7, Issue 8, 2014, Pages 3525-3533

  Neagoe, Victor Emil ^a   Stoica, Radu Mihai ^a   Ciurea, Alexandru Ioan ^a   Bruzzone, Lorenzo ^b   Bovolo, Francesca ^c  

^a UNIVERSITY POLITEHNICA OF BUCHAREST   (Romania)

^b UNIVERSITY OF TRENTO   (Italy)

^c FONDAZIONE BRUNO KESSLER   (Italy)

Author keywords

Concurrent self organizing maps (CSOM); multitemporal images; remote sensing images; supervised unsupervised change detection

Indexed keywords

CHANGE DETECTION; CONCURRENT SELF ORGANIZING MAPS (CSOM); MULTI-TEMPORAL IMAGE; REMOTE SENSING IMAGES;

DETECTION METHOD; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LANDSAT THEMATIC MAPPER; MAP; REMOTE SENSING; SYNTHETIC APERTURE RADAR; TERRA (SATELLITE); UNSUPERVISED CLASSIFICATION;

FUKUSHIMA; HONSHU; JAPAN; MEXICO [NORTH AMERICA]; TOHOKU;

EID: 84908018605     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2330808     Document Type: Article

References (24)

1. L. Bruzzone and D. F. Prieto, "Automatic analysis of the difference image for unsupervised change detection," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1171-1182, May 2000.
2. R. Amelard, A. Wong, and D. A. Clausi, "Unsupervised classification of agricultural land cover using polarimetric synthetic aperture radar via a sparse texture dictionary model," in Proc. Int. Geosci. Remote Sens. Symp. (IGARSS'13), Melbourne, Australia, Jul. 2013, pp. 4383-4386.
3. D. Lu, P. Mausel, E. Brondizion, and E. Moran, "Change detection techniques," Int. J. Remote Sens., vol. 25, no. 12, pp. 2365-2401, Dec. 2004.
4. G. Mazzeo, F. Marchese, C. Filizzola, N. Pergola, and V. Tramutoli, "A multi-temporal robust satellite technique (RST) for forest fire detection," in Proc. Int. Workshop Anal. Multi-Temporal Remote Sens. Images, Leuven, Jul. 2007, pp. 1-6.
5. L. Pulvirenti et al., "Detection of floods and heavy rain using cosmo-skymed data: The event in northwestern Italy of November 2011," in Proc. Int. Geosci. Remote Sens. Symp. (IGARSS'12), Munich, Germany, 2012, pp. 3026-3029.
6. A. Singh, "Digital change detection techniques using remotely-sensed data," Int. J. Remote Sens., vol. 10, no. 6, pp. 989-1003, Jun. 1989.
7. M. V. N. Lima, CORINE Land Cover Updating for the year 2000: IMAGE2000 and CLC2000, Products and Methods, JRC-IES-EEA, Ispra, Italy, Rep. EUR 21757 EN, 2005.
8. CLC2006 Technical Guidelines, EEA, Copenhagen, Denmark, Tech. Rep. 17, pp. 1-70, 2007.
9. F. Bovolo, C. Marin, and L. Bruzzone, "A novel approach to building change detection in very high resolution SAR images," in Proc. 18th SPIE Conf. Image Signal Process. Remote Sens., Edinburgh, U.K., Sep. 2012, pp. 85370Y-01-85370Y-12.
10. F. Bovolo and L. Bruzzone, "A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1658-1670, Jun. 2007.
11. L. Bruzzone and F. Bovolo, "A detail-preserving scale-driven approach to unsupervised change detection in multitemporalSARimages," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 12, pp. 2963-2972, Dec. 2005.
12. W. McGuire, B. McGuire, I. Mason, and C. Kilburn, Natural Hazards and Environmental Change: Key Issues in Environmental Change. New York, NY, USA: Oxford Univ. Press, 2002.
13. F. Bovolo, L. Bruzzone, and M. Marconcini, "A novel approach for unsupervised change detection based on semisupervised SVM and a similarity measure," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2070-2082, Jul. 2008.
14. V. E. Neagoe, R. M. Stoica, and A. I. Ciurea, "A modular neural model for change detection in earth observation imagery," in Proc. Int. Geosci. Remote Sens. Symp. (IGARSS'13), Melbourne, Australia, Jul. 2013, pp. 3321-3324.
15. F. Pacifici, F. Del Frate, C. Solimini, and W. Emery, "An innovative neuralnet method to detect temporal changes in high resolution optical satellite imagery," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 9, pp. 2940-2952, Sep. 2007.
16. A. Salberg and O. D. Trier, "Temporal analysis of multisensor data for forest change detection using hidden Markov models," in Proc. Int. Geosci. Remote Sens. Symp. (IGARSS'12), Munich, Germany, Jul. 2012, pp. 6742-6752.
17. M. Volpi, D. Tuia, F. Bovolo, M. Kanevski, and L. Bruzzone, "Supervised change detection in VHR images using contextual information and support vector machines," Int. J. Appl. Earth Observ. Geoinf., vol. 20, pp. 77-85, Feb. 2012.
18. F. Bovolo, S. Marchesi, and L. Bruzzone, "A framework for automatic and unsupervised detection of multiple changes in multitemporal images," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 6, pp. 2196-2212, Jun. 2012.
19. R. Zurita-Milla, J. A. E. van Gijsel, N. A. S. Hamm, P. W. M. Augustijn, and A. Vrieling, "Exploring spatiotemporal phenological patterns and trajectories using self-organizing maps," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 1914-1921, Apr. 2013.
20. V. E. Neagoe, M. Neghina, and M. Datcu, "Neural network techniques for automated land-cover change detection in multispectral satellite time series imagery," Int. J. Math. Models Methods Appl. Sci., vol. 6, no. 1, pp. 131-139, Jan. 2012.
21. T. Kohonen, Self-Organizing Maps, third extended edition. New York, NY, USA. Springer, 2001.
22. V. E. Neagoe and A. D. Ropot, "Concurrent self-organizing maps for pattern classification," in Proc. 1st IEEE Int. Conf. Cognit. Informat. (ICCI'02), Calgary, Canada, Aug. 2002, pp. 304-312.
23. V. E. Neagoe and G. E. Strugaru, "A concurrent neural network model for pattern recognition in multispectral satellite imagery," in Proc. World Autom. Congr. (WAC'08), Hawaii, USA, Sep./Oct. 2008, pp. 1-6.
24. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.