A New Unsupervised Hyperspectral Band Selection Method Based on Multiobjective Optimization

Unsupervised band selection methods usually assume specific optimization objectives, which may include band or spatial relationship. However, since one objective could only represent parts of hyperspectral characteristics, it is difficult to determine which objective is the most appropriate. In this letter, we propose a new multiobjective optimization-based band selection method, which is able to simultaneously optimize several objectives. The hyperspectral band selection is transformed into a combinational optimization problem, where each band is represented by a binary code. More importantly, to overcome the problem of unique solution selection in traditional multiobjective methods, we develop a new incorporated rank-based solution set concentration approach in the process of Tchebycheff decomposition. The performance of our method is evaluated under the application of hyperspectral imagery classification. Three recently proposed band selection methods are compared.