A comparison between a traditional and a geometrical supervised classifier to produce land cover maps from SPOT5 images - Fiorentino C.; Castrignanò A.; Giglio L.; Di Giacomo E.; Castellini M.; López R.; Ventrella D. (Irrigation in Mediterranean agriculture: challenges and innovation for the next decades, 2008)
The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the opportunity to map ground features, which were not detectable in the past, by using medium resolution remote sensed data (LANDSAT). More accurate and reliable maps of land cover can then be produced. However, classification procedure with these images is more complex than with the medium resolution remote sensing data for two main reasons: firstly, because of their exiguous number of spectral bands, secondly, owing to high spatial resolution, the assumption of pixel independence does not generally hold. It is then necessary to use new spectral classifiers taking into account also proximal information. In this view, it is necessary to combine both spectral and spatial features to optimise land use classification. Standard supervised classification techniques, so-called per-pixel classifiers, use only spectral information of remote sensing image, whereas neglecting the relationships between neighbouring pixels. The objective of this work is the comparison between a conventional supervised classifier, as Maximum Likelihood algorithm, and a spatial classifier based on a searching algorithm of a given geometrical pattern. The data in this study were a remote sensing image taken by SPOT5 satellite in July 2007 and used to discriminate the water melon cover class. Applying the object recognition technique the overall accuracy increased of about 12 percent.
International Conference on Irrigation in Mediterranean Agriculture: Challenges and Innovation for the Next Decades, 2008/06/17-18, Naples (Italy)