Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection

He Sun, Jinchang Ren, Huimin Zhao, Genyun Sun, Wenzhi Liao, Zhenyu Fang, Jaime Zabalza

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Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency in the outcome of UBS. Besides, most of the UBS methods are either relying on complicated measurements or rather noise sensitive, which hinder the efficiency of the determined band subset. In this article, an adaptive distance-based band hierarchy (ADBH) clustering framework is proposed for UBS in HSI, which can help to avoid the noisy bands while reflecting the hierarchical data structure of HSI. With a tree hierarchy-based framework, we can acquire any number of band subset. By introducing a novel adaptive distance into the hierarchy, the similarity between bands and band groups can be computed straightforward while reducing the effect of noisy bands. Experiments on four datasets acquired from two HSI systems have fully validated the superiority of the proposed framework.
Original languageEnglish
Number of pages13
JournalIEEE Transactions on Cybernetics
Early online date24 Mar 2020
Publication statusE-pub ahead of print - 24 Mar 2020


  • adaptive distance
  • hierarchy clustering
  • hyperspectral band selection
  • unsupervised learing


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