Hyperspectral Band Selection via Band Grouping and Adaptive Multi-Graph Constraint

Abstract

Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining. This paper presents a novel unsupervised HSI band selection method via band grouping and adaptive multi-graph constraint. A band grouping strategy that assigns each group different weights to construct a global similarity matrix is applied to address the problem of overlooking strong correlations among adjacent bands. Different from previous studies that are limited to fixed graph constraints, we adjust the weight of the local similarity matrix dynamically to construct a global similarity matrix. By partitioning the HSI cube into several groups, the model is built with a combination of significance ranking and band selection. After establishing the model, we addressed the optimization problem by an iterative algorithm, which updates the global similarity matrix, its corresponding reconstruction weights matrix, the projection, and the pseudo-label matrix to ameliorate each of them synergistically. Extensive experimental results indicate our method outperforms the other five state-of-the-art band selection methods in the publicly available datasets.