Classification and spectral unmixing are two very important tasks for hyperspectral data exploitation. Although
many studies exist in both areas, the combined use of both approaches has not been widely explored in the literature.
Since hyperspectral images are generally dominated by mixed pixels, spectral unmixing can particularly
provide a useful source of information for classification purposes. In previous work, we have demonstrated that
spectral unmixing can be used as an effective approach for feature extraction prior to supervised classification
of hyperspectral data using support vector machines (SVMs). Unmixing-based features do not dramatically
improve classification accuracies with regards to features provided by classic techniques such as the minimum
noise fraction (MNF), but they can provide a better characterization of small classes. Also, these features are
potentially easier to interpret due to their physical meaning (in spectral unmixing, the features represent the
abundances of real materials present in the scene). In this paper, we develop a new strategy for feature extraction
prior to supervised classification of hyperspectral images. The proposed method first performs unsupervised
multidimensional clustering on the original hyperspectral image to implicitly include spatial information in the
process. The cluster centres are then used as representative spectral signatures for a subsequent (partial) unmixing
process, and the resulting features are used as inputs to a standard (supervised) classification process.
The proposed strategy is compared to other classic and unmixing feature extraction methods presented in the
literature. Our experiments, conducted with several reference hyperspectral images widely used for classification
purposes, reveal the effectiveness of the proposed approach.