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Band selection in RKHS for fast nonlinear unmixing of hyperspectral images

Abstract : The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms relevant. With this work, we propose a band selection strategy in reproducing kernel Hilbert spaces that allows to drastically reduce the processing time required by nonlinear unmixing techniques. Simulation results show a complexity reduction of two orders of magnitude without compromising unmixing performance.
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Submitted on : Wednesday, March 8, 2017 - 10:08:44 AM
Last modification on : Thursday, March 18, 2021 - 2:16:08 PM
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  • HAL Id : hal-01485023, version 1
  • OATAO : 17107


Tales Imbiriba, José Carlos Bermudez, Cédric Richard, Jean-Yves Tourneret. Band selection in RKHS for fast nonlinear unmixing of hyperspectral images. 23rd European Signal Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. pp. 1651-1655. ⟨hal-01485023⟩



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