Cluster-Based Ensemble Classification for Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images play a very important role in the discrimination of spectrally similar land-cover classes. In order to obtain a reliable classifier, a larger amount of representative training samples are necessary compared to multi-spectral remote sensing data. In real applications, it is difficult to obtain a sufficient number of training samples for supervised learning. Besides, the training samples may not represent the real distribution of the whole space. To attack the quality problems of training samples, we proposed a Cluster-based ENsemble Algorithm (CENA) for the classification of hyperspectral remote sensing images. Data set collected from ROSIS university validates the effectiveness of the proposed approach.