Abstract
This chapter presents two multimodal prototypes for remote sensing image classification where user interaction is an important part of the system. The first one applies pansharpening techniques to fuse a panchromatic image and a multispectral image of the same scene to obtain a high resolution (HR) multispectral image. Once the HR image has been classified the user can interact with the system to select a class of interest. The pansharpening parameters are then modified to increase the system accuracy for the selected class without deteriorating the performance of the classifier on the other classes. The second prototype utilizes Bayesian modeling and inference to implement active learning and parameter estimation in a binary kernel-based multispectral classification schemes. In the prototype we developed three different strategies for selecting the more informative pixel to be included in the training set. In the experimental section, the prototypes are described and applied to two real multispectral image classification problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Amro, I., Mateos, J., Vega, M.: Parameter estimation in the general contourlet pansharpening method using Bayesian inference. In: 2011 European Signal Processing Conference (EUSIPCO 2011), pp. 1130–1134 (2011)
Amro, I., Mateos, J., Vega, M., Molina, R., Katsaggelos, A.K.: A survey of classical methods and new trends in pansharpening of multispectral images. EURASIP Journal on Advances in Signal Processing 2011(79) (2011)
Bandos, T.V., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 47(3), 862–873 (2009)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer (2007)
Bruzzone, L., Carlin, L., Alparone, L., Baronti, S., Garzelli, A., Nencini, F.: Can multiresolution fusion techniques improve classification accuracy? In: Image and Signal Processing for Remote Sensing XII, vol. 6365, p. 636509 (2006)
Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Trans. on Geoscience and Remote Sensing 43(6), 1351–1362 (2005)
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., Malo, J. (eds.): Remote Sensing Image Processing. Morgan & Claypool Publishers, LaPorte (2011); Bovik, A. (ed.): Collection ‘Synthesis Lectures on Image, Video, and Multimedia Processing’
Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Duda, R., Hart, P.: Pattern classification and scene analysis. Wiley, New York (1973)
Liang, S.: Quantitative Remote Sensing of Land Surfaces. John Wiley & Sons, New York (2004)
Lillesand, T.M., Kiefer, R.W., Chipman, J.: Remote Sensing and Image Interpretation. John Wiley & Sons, New York (2008)
Molina, R., Katsaggelos, A.K., Mateos, J.: Bayesian and regularization methods for hyperparameter estimation in image restoration. IEEE Transactions on Image Processing 8, 231–246 (1999)
Molina, R., Vega, M., Mateos, J., Katsaggelos, A.K.: Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images. Applied and Computational Harmonic Analysis, Special Issue on “Mathematical Imaging”, Part II 24(2), 251–267 (2008)
Ripley, B.D.: Spatial Statistics. Wiley (1981)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Ruiz, P., Mateos, J., Camps-Valls, G., Molina, R., Katsaggelos, A.K.: Bayesian active remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing (submitted, 2012)
Ruiz, P., Talens, J.V., Mateos, J., Molina, R., Katsaggelos, A.K.: Interactive classification oriented superresolution of multispectral images. In: 7th International Workshop Data Analysis in Astronomy ’Livio Scarsi and Vito Di Gesu’ (DAA 2011), pp. 77–85 (2011)
Schölkopf, B., Smola, A.: Learning with Kernels – Support Vector Machines, Regularization, Optimization and Beyond. MIT Press Series, Cambridge (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press (2004)
Tuia, D., Volpi, M., Copa, L., Kanevski, M., Muñoz-Marí, J.: A survey of active learning algorithms for supervised remote sensingimage classification. IEEE Journal on Selected Topics in Signal Processing 4, 606–617 (2011)
Vega, M., Mateos, J., Molina, R., Katsaggelos, A.K.: Super Resolution of Multispectral Images Using TV Image Models. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 408–415. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ruiz, P., Mateos, J., Camps-Valls, G., Molina, R., Katsaggelos, A.K. (2013). Interactive Pansharpening and Active Classification in Remote Sensing. In: Multimodal Interaction in Image and Video Applications. Intelligent Systems Reference Library, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35932-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-35932-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35931-6
Online ISBN: 978-3-642-35932-3
eBook Packages: EngineeringEngineering (R0)