A GEOBIA methodology for fragmented agricultural landscapes
2015
Very high resolution remotely sensed images are an important tool for
monitoring fragmented agricultural landscapes, which allows farmers and policy makers
to make better decisions regarding management practices. An object-based methodology
is proposed for automatic generation of thematic maps of the available classes in the
scene, which combines edge-based and superpixel processing for small agricultural parcels.
The methodology employs superpixels instead of pixels as minimal processing units, and
provides a link between them and meaningful objects (obtained by the edge-based method)
in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by
agricultural small parcels indicates that the combination of both superpixel and edge-based
methods achieves a classification accuracy slightly better than when those methods are
performed separately and comparable to the accuracy of traditional object-based analysis,
with automatic approach.
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