Statistical Classification for Heterogeneous Polarimetric SAR Images

Pierre Formont (1) , Frédéric Pascal (1) , Gabriel Vasile (2) , Jean-Philippe Ovarlez (3) , Laurent Ferro-Famil (4)

1 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
3 ONERA - The French Aerospace Lab [Toulouse]
4 IETR - Institut d'Electronique et de Télécommunications de Rennes

This paper presents a general approach for high-resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test. The new approach provide a threshold over which pixels are rejected from the image, meaning they are not sufficiently "close" from any existing class. A distance measure using this general approach is derived and tested on a high-resolution polarimetric data set acquired by the ONERA RAMSES system. It is compared to the results of the classical decomposition and Wishart classifier under Gaussian and SIRV assumption. Results show that the new approach rejects all pixels from heterogeneous parts of the scene and classifies its Gaussian parts.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Pierre Formont, Frédéric Pascal, Gabriel Vasile, Jean-Philippe Ovarlez, Laurent Ferro-Famil. Statistical Classification for Heterogeneous Polarimetric SAR Images. IEEE Journal of Selected Topics in Signal Processing, 2011, 5 (3), pp.567 - 576. ⟨10.1109/JSTSP.2010.2101579⟩. ⟨hal-00638829⟩