A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

• Main Authors: Chunlin He, Yong Zhang, Dunwei Gong
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Remote Sensing
• Subjects: band selection; artificial bee colony; pseudo-label generation; hypergraph evolutionary clustering
• Online Access: https://www.mdpi.com/2072-4292/12/20/3456

Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant and noise bands. Firstly, replacing traditional pixel points by super-pixel centers, a hypergraph evolutionary clustering method with low computational cost is developed to generate high-quality pseudo-labels; Then, on the basis of these pseudo-labels, taking classification accuracy as the optimized objective, a supervised band selection algorithm based on artificial bee colony is proposed. Moreover, a noise filtering mechanism based on grid division is designed to ensure the accuracy of pseudo-labels. Finally, the proposed algorithm is applied in 3 real datasets and compared with 6 classical band selection algorithms. Experimental results show that the proposed algorithm can obtain a band subset with high classification accuracy for all the three classifiers, KNN, Random Forest, and SVM.