A Parallel Simulated Annealing Approach to Band Selection for Hyperspectral Imagery

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.