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In this paper we present a parallel band selection approach, referred to as parallel simulated annealing band selection (PSABS), for hyperspectral imagery. The approach is based on the simulated annealing band selection (SABS) scheme. The SABS algorithm is originally designed to group highly correlated hyperspectral bands into a smaller subset of band modules regardless of the original order in terms of wavelengths. SABS selects sets of non-correlated hyperspectral bands based on simulated annealing (SA) algorithm and utilizes the inherent separability of different classes in hyperspectral images to reduce dimensionality. In order to be effective, the proposed PSABS is introduced to improve the computational speed by using parallel computing techniques. It allows multiple Markov chains (MMC) to be traced simultaneously and fully utilizes the significant parallelism embedded in SABS to create a set of PSABS modules on each parallel node implemented by the message passing interface (MPI) cluster-based library and the open multi-processing (OpenMP) multicore-based application programming interface. The effectiveness of the proposed PSABS is evaluated by MODIS/ASTER airborne simulator (MASTER) hyperspectral images for hyperspectral band selection during the PACRIM II campaign. The experimental results demonstrated that PSABS can significantly improve the computational loads and provide a more reliable quality of solution compared to the original SABS method.