Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection

Band selection is an important preprocessing step for hyperspectral data processing. It involves two crucial problems, i.e., suitable measure criterion and effective search strategy. Mutual information (MI) has been widely used as the measure criterion for its nonlinear and nonparametric characteristics. For efficient calculation, traditional MI-based criteria commonly use bivariate MI (BMI) to approximate the ideal MI-based criterion. However, these BMI-based criteria may miss the bands having discriminative information and do not give the condition of the approximation. In this paper, a novel criterion based on trivariate MI (TMI) is proposed to measure the redundancy for classification. From the multivariate MI perspective, the proposed TMI-based and traditional BMI-based criteria are proved as the low-order approximations of the ideal criterion under some assumptions. Compared with the BMI-based criteria, a more relaxed assumption condition is required for the TMI-based criterion. To alleviate the problem of few labeled samples existing in hyperspectral images, the TMI-based criterion is extended to the semisupervised TMI-based (STMI) method by adding a graph regulation term. Additionally, to search an appropriate band subset by the TMI- and STMI-based criteria, a new clonal selection algorithm (CSA) is proposed. In CSA, integer encoding and adaptive operators are devised to reduce space and time cost. Experimental results demonstrate the effectiveness of the proposed algorithms for hyperspectral band selection.