Author(s): H. Fizazi Izabatene | R. Rabahi
Journal: Journal of Applied Sciences
ISSN 1812-5654
Volume: 10;
Issue: 8;
Start page: 636;
Date: 2010;
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Keywords: multi-scales | Satellite imaging | classification | Markov random field | pixel | energy
ABSTRACT
The satellite observation with a resolution of ten meters provides images of earth surface. The precise spectral information allows a classification of earth objects. Due to some considerations, Markov Random Field has become a common search procedure. Attention has been focused on utilizing the spatial context in image classification; labels are to be assigned to individuals’ pixels or groups of pixels. In this approach, we relied on, among approaches to Markov Random Field to a supervised classification of satellite images. This approach is known as the Multi-Scales model. We use an energy expression according to Potts model.
Journal: Journal of Applied Sciences
ISSN 1812-5654
Volume: 10;
Issue: 8;
Start page: 636;
Date: 2010;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: multi-scales | Satellite imaging | classification | Markov random field | pixel | energy
ABSTRACT
The satellite observation with a resolution of ten meters provides images of earth surface. The precise spectral information allows a classification of earth objects. Due to some considerations, Markov Random Field has become a common search procedure. Attention has been focused on utilizing the spatial context in image classification; labels are to be assigned to individuals’ pixels or groups of pixels. In this approach, we relied on, among approaches to Markov Random Field to a supervised classification of satellite images. This approach is known as the Multi-Scales model. We use an energy expression according to Potts model.
