Authors:
Selim Hemissi
1
and
Imed Riadh Farah
2
Affiliations:
1
Telecom Bretagne, France
;
2
ENSI, Tunisia
Keyword(s):
Hyperspectral Data, Feature Fusion, Hyperion, Remote Sensing, SVM, Generalized Dirichlet Distribution, Generative/Discriminative Model.
Abstract:
Considering the emergence of hyperspectral sensors, feature fusion has been more and more important for images classification, indexing and retrieval. In this paper, a cooperative fusion method GDD/SVM (Generalized Dirichlet Distribution/Support Vector Machines), which involves heterogeneous features, is proposed for multi-temporal hyperspectral images classification. It differentiates, from most of the previous approaches, by
incorporating the potentials of generative models into a discriminative classifier. Therefore, the multi-features, including the 3D spectral features and textural features, can be integrated with an efficient way into a unified robust framework. The experimental results on a series of Hyperion images confirm the improved performance and show that this cooperative fusion approach has consistence over different testing datasets.