# Band selection in RKHS for fast nonlinear unmixing of hyperspectral images

arXiv: https://arxiv.org/abs/1503.02090 · HTML: https://mlsanity.com/paper/1503.02090/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2015.

## Built on by (most cited first, 1 of 1)

- [A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images](https://mlsanity.com/paper/2306.09418/) (2023)

## Abstract (arXiv)

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.