Contextual PolSAR image classification using fractal dimension and support vector machines

Hossein Aghababaei*, Jalal Amini, Y.C. Tzeng

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)
87 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)317-332
Number of pages16
JournalEuropean Journal of Remote Sensing
Issue number1
Publication statusPublished - 2013
Externally publishedYes




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