Unsupervised classification and spectral unmixing for sub-pixel labelling
The unsupervised classification of hyperspectral images containing mixed pixels is addressed in this paper. Hyperspectral images are characterized by a trade-off between the spectral and the spatial resolution, this leading to data sets containing mixed pixels, e.g. pixels jointly occupied by more than a single land cover class. In [1], a preliminary research based on spectral unmixing concepts was conducted, in order to handle mixed pixels and to obtain thematic maps at a finer spatial resolution. In this work, we extend the investigation by proposing a new methodology based on image clustering. Experiments conducted on real data show the comparative effectiveness of the proposed method, which provides good results in terms of accuracy and is less sensitive to pixels with extreme values of reflectance.