Abstract
Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing.
The paper proposes a data dependent change detection approach based on textural features extracted by the Independent Component Analysis (ICA) model. The properties of ICA allow to create energy features for computing multispectral and multitemporal difference images to be classified. Our experiments on remote sensing images show that the proposed method can efficiently and effectively classify temporal discontinuities corresponding to changed areas over the observed scenes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bosc, M., Heitz, F., Armspach, J. P., Namer, I., Gounot, D., and Rumbach, L. (2003). Automatic change detection in multimodal serial mri: application to multiple sclerosis lesion evolution. Neuroimage, 20:643–656.
Bruzzone, L. and Prieto, D.F. (2002). An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Trans. Image Processing, 11:452–466.
Ceccarelli, N. and Petrosino, A. Multifeature adaptive classifier for sar image segmentation. Neurocomputing, 17:345–363.
Chavez, P.S., Jr. (1989). Radiometric calibration of landsat thematic mapper multispectral images. Photogrammetric Engineering Remote Sensing, 55(9):1285–1294.
Collins, J.B. and Woodcock, C.E. (1996). An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat tm data. Remote Sensing Environment, 56:66–77.
Collins, R., Lipton, A., and Kanade, T. (2000). Introduction to the special section on video surveillance. IEEE Trans. Pattern Anal. Machine Intell., 22(8):745–746.
Dumskyj, M.J., Aldington, S.J., Dore, C.J., and Kohner, E. M. (1996). The accurate assessment of changes in retinal vessel diameter using multiple frame electrocardiograph synchronised fundus photography. Current Eye Research, 15(6):632–652.
Edgington, D., Dirk, W., Salamy, K., Koch, C., Risi, M., and Sherlock, R. (2003). Automated event detection in underwater video. Proc. MTS/IEEE Oceans 2003 Conference.
Fang, C.Y., Chen, S.-W., and Fuh, C.-S. (2003). Automatic change detection of driving environments in a vision-based driver assistance system. IEEE Trans. Neural Networks, 14(3):646–657.
Fung, T. and LeDrew, E. (1988). The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering & Remote Sensing, 54(10):1449–1454.
Harinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Analysis. John Wiley and Sons, Inc.
Huertas, A. and Nevatia, R. (2000). Detecting changes in aerial views of man-made structures. Image and Vision Computing, 18(8):583–596.
Hyvarinen, A. and E., Oja (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9:483–492.
Jain, A.K. and Farrokhnia, F. (1991). Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24(12):1167–1186.
Kan, W.Y., Krogmeier, J.V., and Doerschuk, P.C. (1996). Model-based vehicle tracking from image sequences with an application to road surveillance. Optical Engineering, 35(6):1723–1729.
Landis, E., Nagy, E., Keane, D., and Nagy, G. (1999). A technique to measure 3d work-of-fracture of concrete in compression. J. Engineering Mechanics, 126(6):599–605.
Lebart, K., Trucco, E., and Lane, D.M. (2000). Real-time automatic sea-floor change detection from video. MTS/IEEE OCEANS 2000, pages 337–343.
Lee, T.W. and Lewinksy, M.S. (2002). Unsupervised image classification, segmentation and enhancement using ica mixute models. IEEE Trans. on Image Processing, 11(3):270–279.
Lemieux, L., Wieshmann, U., Moran, N., Fish, D., and Shorvon, S. (1998). The detection and significance of subtle changes in mixed-signal brain lesions by serial mri scan matching and spatial normalization. Medical Image Analysis, 2(3):227–242.
Nagy, G., Zhang, T., Franklin, W., Landis, E., Nagy, E., and Keane, D. (2001). Volume and surface area distributions of cracks in concrete. Visual Form 2001 (Springer LNCS 2059), pages 759–768.
Randen, T. and Husoy, J.H. (1999). Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):291–310.
Rey, D., Subsol, G., Delingette, H., and Ayache, N. (2002). Automatic detection and segmentation of evolving processes in 3d medical images: Application to multiple sclerosis. Medical Image Analysis, 6(2):163–179.
Singh, A. (1989). Digital change detection techniques using remotely-sensed data. Internat. Journal of Remote Sensing, 10(6):989–1003.
Slater, P.N. (1987). Reflectance and radiance based methods for the in-flight absolute calibration of multispectral sensors. Remote Sensing of Environment, 22:11–37.
Stauffer, C. and Grimson, W. E. L. (2000). Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Machine Intell., 22(8):747–757.
Thirion, J.-P. and Calmon, G. (1999). Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences. IEEE Transactions on Medical Image Analysis, 18(5):429–441.
Turner, M. R. (1986). Texture discrimination by gabor functions. Biol. Cybern., 55:71–82.
Whorff, J. and Griffing, L. (1992). A video recording and analysis system used to sample intertidal communities. Journal of Experimental Marine Biology and Ecology, 160:1–12.
Wren, C. R., Azarbayejani, A., Darrell, T., and Pentland, A. (1997). Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Anal. Machine Intell., 19(7):780–785.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer
About this paper
Cite this paper
Antoniol, G., Ceccarelli, M., Petrillo, P., Petrosino, A. (2005). An ICA Approach to Unsupervised Change Detection in Multispectral Images. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_35
Download citation
DOI: https://doi.org/10.1007/1-4020-3432-6_35
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
eBook Packages: Computer ScienceComputer Science (R0)