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Unsupervised Hyperspectral Band Selection Using Clustering and Single-layer Neural Network

Abstract : Hyperspectral images provide rich spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality. Thus, band selection is a practice that has been adopted before any further processing takes place. Therefore, in this paper, a new unsupervised method for band selection based on clustering and neural network is proposed. A comparison with six other band selection frameworks shows the strength of the proposed method.
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  • HAL Id : hal-01880365, version 1

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Mateus Habermann, Vincent Frémont, Elcio Shiguemori. Unsupervised Hyperspectral Band Selection Using Clustering and Single-layer Neural Network. Revue Française de Photogrammétrie et de Télédétection, Société Française de Photogrammétrie et de Télédétection, 2018, pp.33-42. ⟨hal-01880365⟩

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