Hyperspectral Remote Sensing Image Band Selection Via Multi-Objective Sine Cosine Algorithm

For hyper-spectral image band selection, there are two main key concerns, which are the curial information preservation and the redundancy information reduction. Since the two objectives are contradictory, the single-objective based band selection methods are usually unsatisfactory, thus, a superior approach which can obtain a trade-off between them is needed. In order to address this problem, an evolutionary computation method called sine cosine algorithm which has the capabilities of the global exploration and the local exploitation is applied, and its multi-objective discrete version is designed for multi-objective band selection. In addition, in this paper, two novel measures are utilized for meeting the requirements. The effectiveness of the proposed method is confirmed by the experimental results obtained with two real hyper-spectral images.