An automatic band selection algorithm based on connection centre evolution. Issue 4 (3rd April 2023) - Tang, Hairong; Zhang, Yu; Zhang, Peng; Ji, Luyan
ABSTRACT: The characteristics of high dimensionality, strong band correlation, and high information redundancy make band selection become a necessary step for efficient processing of hyperspectral images. In this paper, a new band selection method based on connection centre evolution (CCE) is proposed, named n -nearest neighbour CCE band selection (NNCCEBS). First, according to CCE, we measure the band similarity under different observation scales by iteratively calculating the connectivity at different scales. Therefore, NNCCEBS can achieve multi-scale band selection, and is able to complete it at one time. Next, to avoid the selected bands being aggregated, an n -nearest neighbour similarity matrix is introduced, which only retains the connectivity of n -nearest bands for a band. By doing so, the new method is less sensitive to the parameter settings, and can select more accurate band centres. Experiments on real hyperspectral datasets prove the effectiveness of our method.