Two major issues encountered in unsupervised hyperspectral image classification are (1) how to determine the number
of spectral classes in the image and (2) how to find training samples that well represent each of spectral classes without
prior knowledge. A recently developed concept, Virtual dimensionality (VD) is used to estimate the number of spectral
classes of interest in the image data. This paper proposes an effective algorithm to generate an appropriate training set
via a recently developed Prioritized Independent Component Analysis (PICA). Two sets of hyperspectral data,
Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Cuprite data and HYperspectral Digital Image Collection
Experiment (HYDICE) data are used for experiments and performance analysis for the proposed method.