Remote Sensing (Oct 2022)

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

  • Mateus Habermann,
  • Elcio Hideiti Shiguemori,
  • Vincent Frémont

DOI
https://doi.org/10.3390/rs14215374
Journal volume & issue
Vol. 14, no. 21
p. 5374

Abstract

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A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes phenomenon. In order to attenuate such problems, one can resort to dimensionality reduction (DR). Thus, this paper proposes a new DR algorithm, which performs an unsupervised band selection technique following a clustering approach. More specifically, the data set was split into a predefined number of clusters, after which the bands were iteratively selected based on the parameters of a separating hyperplane, which provided the best separation in the feature space, in a one-versus-all scenario. Then, a fine-tuning of the initially selected bands took place based on the separability of clusters. A comparison with five other state-of-the-art frameworks shows that the proposed method achieved the best classification results in 60% of the experiments.

Keywords