Classification of oyster habitats by combining wavelet-based texture features and polarimetric SAR descriptors

Olivier Regniers (1, 2) , Lionel Bombrun (1) , Ioana Ilea (1) , Virginie Lafon (2) , Christian Germain (1)

1 IMS - Laboratoire de l'intégration, du matériau au système
2 GEO Transfert

In this study, we propose to evaluate the potential of combining very high resolution optical and SAR images for the classification of oyster habitats in tidal flats. To describe the classes of interest in both data, features are extracted by using wavelet-based texture features and polarimetric inter-band dependencies. A multisensor fusion scheme is then applied by adopting a maximum probability rule based on the outputs of SVM classifiers. Classification results show higher accuracies of detection of cultivated and abandoned oyster fields in comparison to classifications obtained using only texture features. This demonstrate the benefit of using both optical and SAR data for oyster habitats mapping in tidal flats.

Mots clés: texture; multi-sensor fusion; wavelet; SVM; classification; very high resolution; oyster habitats

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

Olivier Regniers, Lionel Bombrun, Ioana Ilea, Virginie Lafon, Christian Germain. Classification of oyster habitats by combining wavelet-based texture features and polarimetric SAR descriptors. IEEE International Geoscience and Remote Sensing Symposium 2015, 2015, Milan, Italy. pp.3890-3893. ⟨hal-01188201⟩