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In this paper a parallel band selection approach, referred to as parallel simulated annealing band selection (PSABS), is presented for high-dimensional remote sensing images. The approach is based on the simulated annealing band selection (SABS) scheme which is originally designed to group highly correlated hyperspectral bands into a smaller subset of modules regardless of the original order in terms of wavelengths. SABS selects sets of correlated hyperspectral bands based on simulated annealing (SA) algorithm and utilizes the inherent separability of different classes to reduce dimensionality. In order to be effective, the proposed PSABS is introduced to improve the computational performance by using parallel computing technique. It allows multiple Markov chains (MMC) to be traced simultaneously and fully utilizes the parallelism of SABS to create a set of SABS modules on each parallel node. Two parallel implementations, namely the message passing interface (MPI) cluster-based library and the open multi-processing (OpenMP) multicore-based application programming interface, are applied to three different MMC techniques: non-interacting MMC, periodic exchange MMC and asynchronous MMC for evaluation. The effectiveness of the proposed PSABS is evaluated by NASA MODIS/ASTER (MASTER) airborne simulator data sets and airborne synthetic aperture radar (SAR) images for land cover classification during the Pacrim II campaign in the experiments. The results demonstrated that the MMC techniques of PSABS can significantly improve the computational performance and provide a more reliable quality of solution compared to the original SABS method.