Hyperspectral band selection based on multi-objective optimization with high information and low redundancy

For band selection in hyperspectral images, there are two crucial aspects, information preservation and redundancy reduction, which have a great influence on the subsequent applications. Most of current works have a fixed preference on these two aspects. However, since different data sets possess different numerical characteristics, optimal preferences on these two aspects may be different and difficult to be decided. Therefore, a method is required, which can explore optimal trade-offs of these two aspects according to different characteristics of data sets. To address this challenge, a novel multi-objective optimization model for band selection is proposed. Two conflicting objective functions compose the proposed model. One measures the amount of information and the other one measures the redundancy contained in the selected bands. Through this model, the two aspects are quantified, which makes it possible to optimize them simultaneously. To optimize this model, a new multi-objective immune algorithm is designed to fit the characteristics of hyperspectral data. And it has the ability to obtain a series of Pareto optimal solutions which represent different optimal trade-offs between the two objective functions. In this way, the proposed method can explore the optimal trade-offs between the two aspects and provide decision makers more options according to different characteristics of data sets. Experiments are implemented on three real hyperspectral data sets. The results show the superiority of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.