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Entropy-Alpha classification alternative for polarimetric SAR image

Jaan Praks (1)and Martti Hallikainen(1)

(1) Helsinki University of Technology, P.O. Box 3000, FIN-02015 HUT, Finland

Abstract

In this work we describe how coherency matrix simple invariants can be used to create similar classification parameter space to the well-known Entropy-Alpha classification. Proposed parameter pair is very simple and fast to calculate with almost any remote sensing oriented image- processing software if polarimetric data is given in covariance matrix (or comparable) format. Calculations employ only linear combinations of absolute values of the coherency matrix elements. Proposed parameters are related to entropy and alpha with some ambiguity and classification results differ slightly from entropy-alpha classification. Classification results between entropy-alpha and proposed scheme differ only for 3% of an example image pixels. As an example, NASA/JPL AIRSAR L-Band image for the San Francisco bay was classified with both algorithms. Size of the used image was 224 x 256 pixels. Both algorithms classified 97 % of pixels into corresponding classes. Highest misclassification rates are for high entropy classes. Method is suitable for using as fast approximation for the entropy-alpha classification or independent classification scheme.

 

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