A robust and efficient band selection method using graph representation for hyperspectral imagery

In the field of unsupervised band selection, both robustness and efficiency are of great importance. In this article, we propose a new unsupervised band selection method termed graph representation based band selection GRBS, which is expected to be insensitive to noisy bands and computationally inexpensive. In GRBS, bands are treated as the nodes of graph in high-dimensional space and centres of the band clusters are considered as the ideal choice. Interestingly, different from other clustering-based band selection methods, GRBS does not involve band clustering. Instead, it employs an easily computed criterion function to select the desired bands, which greatly improves the efficiency. The experiments demonstrate that GRBS has a promising performance and outperforms the compared methods in terms of both accuracy and efficiency.