In this paper, a new classification scheme of polarimetric synthetic aperture radar (PolSAR) images using fractal dimension as contextual information is proposed. Support vector machines (SVM) due to their ability to handle the nonlinear classifier problem are applied to a new fractal feature vector, which is constructed from Pauli decomposed vector and fractal dimensions. Fractal dimension is computed based on the concepts of fractional Brownian motion (fBm) and wavelet multi-resolution analysis using a self-adaptive window approach and fuzzy logic. The experimental results on AIRSAR images prove effectiveness of the proposed vector in comparison to the Pauli decomposed vector.
Aghababaei, H., Amini, J., & Tzeng, Y. C. (2013). Contextual PolSAR image classification using fractal dimension and support vector machines. European Journal of Remote Sensing , 46(1), 317-332. https://doi.org/10.5721/EuJRS20134618