In this study we apply a variant of a recently proposed linear subspace method, the neighbourhood component analysis (NCA), to the task of hyperspectral classification. The NCA algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. NCA assumes nothing about the form of the each class and the shape of the separating surfaces. Experimental studies were conducted on the basis of hyperspectral images acquired by two sensors: the airborne visible/infrared imaging spectroradiometer (AVIRIS) and AISA-EAGLE. Experimental results confirm the significant superiority of the NCA classifier in the context of hyperspectral data classification over methodologies that were previously suggested.
['Lior Weizman', 'Jacob Goldberger']
A classification-based linear projection of labeled hyperspectral data