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Journal Articles IEEE Geoscience and Remote Sensing Letters Year : 2018

Morphological Band Selection for Hyperspectral Imagery

Jingyu Wang
Xianyu Wang
Ke Zhang
  • Function : Author

Abstract

In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data. For the obtained clusters, the differences of spectral response curves are applied to measure the local discrimination capability of bands, of which the local maximum value point is yielded based on the morphological processing. To verify the performance of the proposed method, the robustness of the parameters has been evaluated, while the effectiveness and superiority have been tested on three popular hyperspectral data sets. The experiment results have shown that the proposed method outperforms other methods.
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Dates and versions

hal-04318288 , version 1 (01-12-2023)

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Jingyu Wang, Xianyu Wang, Ke Zhang, Kurosh Madani, Christophe Sabourin. Morphological Band Selection for Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Letters, 2018, 15 (8), pp.1259-1263. ⟨10.1109/LGRS.2018.2830795⟩. ⟨hal-04318288⟩

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