Weighted Wishart distance learning for PolSAR image classification

ABSTRACT An approach of weighted Wishart distance learning, shorted for W2-based distance learning, is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. It aims to adjust the Wishart distance by enhancing discrimination as well as exploiting spatial information. The proposed distance learning keeps samples within the same category close and separates samples from the different classes far apart. It is effectively implemented by solving a linear programming. Input of W2-based distance learning is called weighted Wishart feature, which is designed specifically for PolSAR data to describe the Wishart distribution, achieve regional consistency, and reduce speckle noise. Weight is calculated according to an adaptive window, where homogeneous samples are derived based on a connected region and extracted edge information. With this feature, W2-based distance learning is a whole scheme to adjust the Wishart distance. Furthermore, our experiments with benchmark data sets suggest that the proposed scheme provides both improved performance in terms of visual effect and classification accuracy. The achieved overall accuracy is better by more than 7% compared to other state-of-art methods.

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