1 research outputs found
Satellite image classification and segmentation using non-additive entropy
Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis
entropy on the pattern recognition and segmentation of coloured images obtained
by satellites, via "Google Earth". By segmentation we mean split an image to
locate regions of interest. Here, we discriminate and define an image partition
classes according to a training basis. This training basis consists of three
pattern classes: aquatic, urban and vegetation regions. Our numerical
experiments demonstrate that the Tsallis entropy, used as a feature vector
composed of distinct entropic indexes outperforms the standard entropy.
There are several applications of our proposed methodology, once satellite
images can be used to monitor migration form rural to urban regions,
agricultural activities, oil spreading on the ocean etc.Comment: 4 pages, 5 figures, ICMSquare 201