Abstract
The polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For the scene interpretation, it is desirable to separate between the target response and the speckle. For this purpose, we proposed a new speckle reduction approach using independent component analysis (ICA) based on statistical formulation of PSAR image. In addition, we apply four ICA algorithms on real PSAR images and compare their performances. The comparison reveals characteristic differences between the studied neural ICA algorithms, complementing the results obtained earlier.
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
Oliver, C., Quegan, S.: Understanding Synthetic Aperture Radar Images. Artech-House, London (1998)
Chitroub, S., Houacine, A., Sansal, B.: Statistical characterisation and modelling of SAR images. Signal Processing 82(1), 69–92 (2002)
Pi, Y., et al.: Polarimetric speckle reduction using multi-texture maximum likelihood method. IEE Electronic Letter 39(18), 1348–1349 (2003)
Common, P.: Independent component analysis, a new concept? Signal processing 36, 287–314 (1994)
Cichocki, A., Unbehauen, R.: Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Trans. on Circuits and Systems 43(11), 894–906 (1996)
Yang, H., Amari, S.: Adaptive online learning algorithms for blind separation: Maximum entropy and minimum mutual information. Neural Computation 9(7), 1457–1482 (1997)
Cruces, S., Castedo, L., Cichocki, A.: Robust blind source separation algorithms using cumulants. Neurocomputing 49, 87–118 (2002)
Lee, T.-W., Girolami, M., Sejnowski, T.J.: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources [J]. Neural Computation 11(2), 417–441 (1999)
Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Fiori, S.: Overview of independent component analysis technique with an application to Synthetic Aperture Radar (SAR) imagery processing. Neural Networks 16(special issue), 453–467 (2003)
Giannakopoulos, X., Karhunen, J., Oja, E.: An experimental comparison of neural ICA algorithms. In: Proc. Int. Conf. on Artificial Neural Networks, Girolami, M., Fyfe, C., Generalised independent, Skovde, Sweden (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ji, J., Tian, Z. (2006). Speckle Reduction of Polarimetric SAR Images Based on Neural ICA. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_43
Download citation
DOI: https://doi.org/10.1007/11893257_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)