Band Selection of Hyperspectral Images Using Multiobjective Optimization-Based Sparse Self-Representation

Hyperspectral images (HSIs) consist of hundreds of continuous bands with high correlation, making it contain great abundant information. Band selection is an effective idea for removing redundant bands and preserving the physical significance at the same time. Popular sparse representation-based band selection commonly introduces an additional coefficient to combine error term and sparse constraint term, making it difficult to find out the optimal balance coefficient. In this letter, we propose a hybrid clustering-based band-selection approach based on using evolutionary multiobjective optimization to solve a sparse self-representation model constituted with two conflicting objectives. The proposed approach simultaneously minimizes two terms of the sparse representation model, avoiding the balance coefficient and producing a set of optimal solutions that are used to construct a similarity matrix for spectral clustering. Finally, a reduced band subset is determined by the cluster centers. We compare the results of the proposed approach with four existing band-selection methods for three real HSI data sets, showing that the proposed approach is able to effectively select representative bands with better classification accuracy.