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Performance of a fast iterative algorithm for unsupervised Bayesian classification of multispectral and hyperspectral images

Image classification based on radiometric and spectral information is connected with histogram labelling, therefore the required computing time exponentially grows with increasing pixel depth (m) and the number of spectral bands (M). In fact, histogram labelling requires the estimation of a cost function summed over every state of the input image, for a total number of states N = 2mM. Various techniques have been exploited in order to overcome this difficulty, but no general and satisfactory solution has been pointed out yet. We developed an iterative fitting algorithm in which the image histogram is analysed in the neighbourhood of its peaks. Each peak is fitted independently from other, using only local histogram data that feed an iterative fast procedure. Once the current peak has been processed, the input histogram is cleared from its contributions, and the residual histogram maximum is processed. The performance of the algorithm has been investigated by processing several TM images and photogrammetry. Executed tests have shown a good ability of the algorithm to accurately recognise different image classes, performing the entire classification in a very short time.