For hyperspectral imagery, simulated annealing (SA) and greedy modular eigenspaces (GME) have been successfully developed to cluster highly correlated hyperspectral bands into a smaller subset of band modules. This paper introduces a novel band selection technique of combining these two approaches, called the SA and GME band selection (SGBS), for hyperspectral imagery. The SGBS selects sets of non-correlated hyperspectral bands for hyperspectral images based on heuristic and greedy algorithms, utilizes the inherent separability of different classes in hyperspectral images to reduce dimensionality, and further generates a unique clustered eigenspace (CE) feature set effectively. The proposed SGBS features can 1) avoid the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis, 2) evince improved discriminatory properties, crucial to subsequent classification compared with conventional band selection techniques, 3) provide a fast procedure to simultaneously select the most significant features by merging SA and GME schemes, and 4) select each band by a simple logical operation, called the CE feature scale uniformity transformation (CE/FSUT), to include different classes into the most common feature modules of the hyperspectral bands. The performance of the proposed SGBS method was evaluated by MODIS/ASTER airborne simulator (MASTER) images for land cover classification during the Pacific Rim II campaign. Encouraging experimental results showed that the proposed SGBS approach is effective and can be used as an alternative to the existing band selection algorithms.