Correntropy-Based Sparse Spectral Clustering for Hyperspectral Band Selection

IEEE Geoscience and Remote Sensing Letters, pp. 484-488, 2020.

Cited by: 3|Bibtex|Views23|DOI:https://doi.org/10.1109/LGRS.2019.2924934
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Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

This letter presents a correntropy-based sparse spectral clustering (CSSC) method to select proper bands of a hyperspectral image. The CSSC first constructs an affinity matrix with the correntropy measure which considers the nonlinear characteristics of hyperspectral bands and can suppress effects from noise or outliers in measuring band ...More

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