Speckle Reduction of Polarimetric SAR Images Based on Neural ICA
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.
KeywordsIndependent Component Analysis Synthetic Aperture Radar Independent Component Analysis Synthetic Aperture Radar Image Blind Source Separation
Unable to display preview. Download preview PDF.
- 1.Oliver, C., Quegan, S.: Understanding Synthetic Aperture Radar Images. Artech-House, London (1998)Google Scholar
- 11.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)Google Scholar