Towards Weakly Pareto Optimal: An Improved Multi-Objective Based Band Selection Method for Hyperspectral Imagery

Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. Owing to the parameters independence and the optimal solutions, multi-objective based methods have presented promising performance. However, the characteristics of the hyperspectral band selection problem make its range to be discrete. In this case, recently proposed weighted Tchebycheff based multi-objective band selection methods could only reach the weakly Pareto optimal, which would result in non-unique solutions. In this paper, we improve the decomposition process of the multi-objective based band selection method via a boundary intersection approach. Compared with weighted Tchebycheff decomposition, the proposed method is able to change the shape of the contour lines between Pareto Front and the ideal point, and this approach is particularly suitable for discrete-range problems. The effectiveness of our improvement is demonstrated by comparison experiments.