GPU implementation of ant colony optimization-based band selections for hyperspectral data classification

Band selection (BS) is an important dimensionality reduction procedure in hyperspectral data processing, which selects a subset of original bands that contain the most useful information about objects. Ant Colony Optimization (ACO) algorithm was recently introduced for band selection from hyperspectral images. This algorithm has been demonstrated it could select satisfactory results in experimental analysis. However, the ACO-based band selection (ACOBS) is time-consuming for hyperspectral image analysis due to its high computational amount. In this paper, the high-performance computing technology based on the Graphics Processing Units (GPUs) was utilized to improve the computational efficiency of the ACOBS algorithm. The experimental results showed that the computational performance of ACOBS based on GPU was significantly improved in the analysis of real hyperspectral data.