Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
High-order contrasts for independent component analysis
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Intelligent Data Analysis
Comparison of polarimetric SAR observables in terms of classification performance
International Journal of Remote Sensing
Applications of ICA for the enhancement and classification of polarimetric SAR images
International Journal of Remote Sensing
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
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In remotely sensed Synthetic Aperture Radar (SAR) images, scattering from a target is often the result of a mixture of different mechanisms. For this reason, detection of targets and classification of SAR images may be very difficult and very different from other sensor imagery. Fully polarimetric data offer the possibility to separate the different mechanisms, interpret them and consequently identify the geometry of the targets. To achieve this task, several target decomposition techniques have been proposed in the literature to improve the interpretation of this kind of data. Among these, the physical based techniques are the most considered. This paper proposes a novel approach for target decomposition based on the use of Nonlinear Principal Component Analysis. Different from physical based target decomposition techniques, the proposed method is based on a nonlinear decorrelation of the received polarimetric SAR (POLSAR) signal into few elementary components that could be associated to the different scattering mechanisms present in the image. A comparison of the classification results obtained using different decomposition techniques demonstrates that the proposed approach can be an effective alternative to classical physical based methods.