Multicriteria semi-supervised hyperspectral band selection based on evolutionary multitask optimization

Band selection is a direct and effective method to reduce the spectral dimension, which is one of popular topics in hyperspectral remote sensing. Compared with unsupervised band selection methods, semi-supervised methods seek not only informative but also discriminative band subset by using both labeled and unlabeled samples. However, most currently semi-supervised selection methods simply use a unified criterion on both labeled and unlabeled samples for searching optimal bands, which lacks sample pertinence and adds calculation burden. Since different samples possess different numerical characteristics, optimal criterion on these two kinds of samples may be different. Therefore, a method is required, which can concentrate on the characteristics of labeled and unlabeled samples providing different measure criteria to utilize samples more purposefully. In this paper, a multicriteria semi-supervised model is designed for hyperspectral images band selection. The model is established into two specific tasks: One task measures the amount of information and the redundancy contained in the selected bands from unlabeled samples, the other task utilizes the labeled samples to measure the discrimination of the selected bands. To optimize this model, a multitask optimization strategy is designed to merge the bands information and accelerate the speed of searching the promising bands. In addition, the de-duplication genetic operators are designed to fit the characteristics of hyperspectral images. In this way, the proposed multitask band selection method can select bands with high information, high discrimination, and low redundancy from hyperspectral data in an efficient way according to fully exploiting the numerical characteristics of both labeled and unlabeled samples. Experimental results show the superiority of the proposed method, and demonstrate that the proposed model works more efficiently than the comparison band selection methods.