Fusion of supervised and unsupervised learning for improved classification of hyperspectral images

In this paper, we introduce a novel framework for improved classification of hyperspectral images based on the combination of supervised and unsupervised learning paradigms. In particular, we propose to fuse the capabilities of the support vector machine classifier and the fuzzy C-means clustering algorithm. While the former is used to generate a spectral-based classification map, the latter is adopted to provide an ensemble of clustering maps. To reduce the computation complexity, the most representative spectral channels identified by the Markov Fisher Selector algorithm are used during the clustering process. Then, these maps are successively labeled via a pairwise relabeling procedure with respect to the pixel-based classification map using voting rules. To generate the final classification result, we propose to aggregate the obtained set of spectro-spatial maps through different fusion methods based on voting rules and Markov Random Field theory. Experimental results obtained on two hyperspectral images acquired by the reflective optics system imaging spectrometer and the airborne visible/infrared imaging spectrometer, respectively; confirm the promising capabilities of the proposed framework.