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Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

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4 Author(s)
Mingmin Chi ; Sch. of Comput. Sci., Fudan Univ., Shanghai, China ; Qian Kun ; Benediktsson, J.A. ; Rui Feng

In real applications, it is difficult to obtain a sufficient number of training samples in supervised classification of hyperspectral remote sensing images. Furthermore, the training samples may not represent the real distribution of the whole space. To attack these problems, an ensemble algorithm which combines generative (mixture of Gaussians) and discriminative (support cluster machine) models for classification is proposed. Experimental results carried out on hyperspectral data set collected by the reflective optics system imaging spectrometer sensor, validates the effectiveness of the proposed approach.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:6 ,  Issue: 4 )