LAUSR

ABSTRACT Hyperspectral image (HSI) has become one of the most important remote sensing techniques for object interpretation by its abundant band information. As the data dimension increases, band selection technique is utilized to achieve the highest possible classification accuracy with fewer bands. Essentially, it is considered as an NP-hard problem, which is a non-deterministic problem within polynomial complexity and difficult to achieve a satisfactory solution using traditional search methods. Sine cosine algorithm (SCA) is a recently developed swarm intelligence algorithm based on the calculation of sine and cosine functions. To determine the parameters setting in SCA, Lévy flight technique is employed to improve the exploitation phase of the algorithm. In the paper, a new band selection method on the basis of SCA with a Lévy flight is proposed, and an alternative distribution is utilized to decrease the band dimension of HSI. In addition, crossover operation is conducted to enhance the optimal bits of each individual, and an evaluation criterion for assessing the performance of band selection is defined, allowing the classification accuracy and selected number of bands to be as balanced as possible. Experimental results demonstrate that the proposed band selection method is superior to other state-of-the-art approaches in terms of band subsets that achieve higher classification accuracy.