Band selection, as an effective and popular dimensional reduction methods for hyperspectral image (HSI), has raised wide attention in recent years. In this paper, we propose a novel band selection method called optimal neighboring reconstruction (ONR). Compared to conventional methods, ONR mainly has following advantages. 1) It is globally optimal, which means the best combination of bands towards the designed objective function can be achieved. 2) It sufficiently exploits the neighboring structure among bands, so can effectively reduce the redundancy while maintaining the discrimination among bands. Experiments on three real data sets show that the proposed method has excellent performance. © 2018 IEEE