The Study of The Bay of Mount Saint-Michel by Using Graph Theory in The Analysis of Satellite Images
In this paper, a new approach for mapping based on the concept of objects and relationships between these objects is proposed to take advantage from both supervised and unsupervased classification methods. On the one hand, objects obtained after a supervised classification are represented by an adjacency graph model. On the other hand, objects obtained after unsupervised classification are represented by an adjacency graph data, and the goal is to measure the matching between this two graphs in order to improve the results of unsupervised classification in association with those obtained from supervised classification. This study concerned the coastal Bay of Mont Saint-Michel, the data used are from SPOT 5 optical satellite images.
Keywords: Clustering, graph theory, Classification, graph matching, spatial relations, mapping.
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ABOUT THE AUTHOR
Bouraoui Seyfallah
I\'am PhD since 2010 in the team Global Dynamics and Active Deformation at the University of Strasbourg, France. I\'m working on remote sensing: Processing both of optical image and radar signal processing. I am studying the ground deformation due to humain activity, landslides, faults ... All about surface deformation using advanced InSAR technics (permanent scatterer and Small Baseline) with merged methods.
Bouraoui Seyfallah
I\'am PhD since 2010 in the team Global Dynamics and Active Deformation at the University of Strasbourg, France. I\'m working on remote sensing: Processing both of optical image and radar signal processing. I am studying the ground deformation due to humain activity, landslides, faults ... All about surface deformation using advanced InSAR technics (permanent scatterer and Small Baseline) with merged methods.
