| Paper: | MLSP-P5.11 | ||
| Session: | Image and Video Processing | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Image and Video Processing Applications | ||
| Title: | PCA-ICA NEURAL NETWORK MODEL FOR POLSAR IMAGES ANALYSIS | ||
| Authors: | Salim Chitroub; Electronics and Informatics Faculty, U. S. T. H. B | ||
| Abstract: | The POLSAR 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, a PCA-ICA neural network model is proposed. Based on its rigorous statistical formulation, a neuronal approach for the simultaneous diagonalisation of the signal and noise covariance matrices using PCA transform is proposed. The PC images are uncorrelated and having an improved SNR. However, the speckle is a non-Gaussian multiplicative noise, the higher order statistics contain an additional information about it. ICA method is used to separate the speckle from the PC images and providing new IC images that have an improved contrast. The method has been applied on real POLSAR images. The extracted features are quite effective for the scene interpretation. | ||
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