Segmentation of hedges on CASI hyperspectral images by data fusion from texture, spectral and shape analysis

The study figures out the potential of CASI airborne hyperspectral imagery for the fine segmentation and characterization of small size landscape units, the hedges, essential for hydrologists and landscape planners. The segmentation strategy consists in computing every hedge discriminating feature: radiometry, texture and linear shape. Original methods taking into consideration the full spectral information are developed for filtering images and computing linear and texture features. Concepts of fuzzy fusion are used to merge these information in order to get the final segmented image. Classification of the segmented region provides the bocage composition map. With the help of a DEM, 8 parameters are computed, providing a fine characterization for each pixel of the bocage.