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With the definite labeled samples, it is difficult that avoid the curse of the dimensionality for the accurate and efficient classification of hyperspectral images. It is necessary that reduce the dimensionality of hyperspectral images. Therefore, Polymorphic Ant Colony Algorithm (PACA) based band selection algorithm (PACA-BS) for hyperspectral images is proposed in this paper. Compared with the common Ant Colony Algorithm (ACA) based band selection algorithm (ACA-BS), PACA-BS can significantly decrease the searching space and thus the time complexity. These algorithms are applied to select the bands of Hyperion and AVIRIS hyperspectral image according to the class separability criterion. Performance evaluation of algorithms is focused on the following aspects: computing time and overall classification accuracy. The results showed that the computing time of PACA-BS was markedly lower than ACA-BS. Furthermore, band sets of PACA-BS generate a higher overall classification accuracy. The PACA-BS is thus proved to be a promising and optimized method for band selection of hyperspectral image.

A Polymorphic Ant Colony Algorithm (PACA) for the Selection of Optimized Band Selection of Hyperspectral Remote Sensing Image