Wavelet based texture modeling for panchromatic very high resolution image classification : application to oyster racks detection

Olivier Regniers (1) , Lionel Bombrun (1) , Virginie Lafon (2) , Aurélie Dehouck (2) , Claire Tinel (3) , Christian Germain (1)

1 IMS - Laboratoire de l'intégration, du matériau au système
2 GEO Transfert
3 CNES - Centre National d'Études Spatiales [Toulouse]

This study evaluates the potential of wavelet-based texture multivariate modeling for the detection of cultivated oyster fields and their differentiation from abandoned fields in Very High Resolution panchromatic PLEIADES data. The proposed models are tested in a supervised classification context using a training database composed of extracted image patches representative of the land covers of interest. The obtained classification results exhibit high detection rate for cultivated fields. Classification errors appear however in the detection of abandoned fields. These results demonstrate the interest of such model for the characterization of inter-tidal ecosystems and opens up perspectives for their use in the management of oyster farming activities.

Mots clés: Texture analysis; classification; very high resolution; wavelet; oyster culture

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

Olivier Regniers, Lionel Bombrun, Virginie Lafon, Aurélie Dehouck, Claire Tinel, et al.. Wavelet based texture modeling for panchromatic very high resolution image classification : application to oyster racks detection. IEEE International Geoscience and Remote Sensing conference, 2014, Québec, Canada. pp.5148-5151. ⟨hal-01064413⟩