Title :
Wavelet based texture modeling for panchromatic very high resolution image classification: Application to oyster racks detection
Author :
Regniers, O. ; Bombrun, L. ; Lafon, V. ; Dehouck, A. ; Tinel, C. ; Germain, C.
Author_Institution :
Lab. IMS, Univ. of Bordeaux, Talence, France
Abstract :
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.
Keywords :
ecology; farming; feature extraction; geophysical image processing; image classification; image resolution; image texture; land cover; vegetation mapping; abandoned field detection; classification errors; cultivated fields; image patch extraction; intertidal ecosystem characterization; land covers; oyster farming activities; oyster racks detection; panchromatic very high resolution image classification; training database; very high resolution panchromatic PLEIADES data; wavelet based texture modeling; Biological system modeling; Feature extraction; Image resolution; Object oriented modeling; Remote sensing; Training; Vectors; Texture analysis; classification; oyster culture; very high resolution; wavelet;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
DOI :
10.1109/IGARSS.2014.6947657