Band selection in RKHS for fast nonlinear unmixing of hyperspectral images
This addresses efficiency issues for researchers and practitioners in remote sensing and image processing, though it is incremental as it builds on existing nonlinear unmixing methods.
The paper tackled the high computational cost of nonlinear unmixing in hyperspectral images by proposing a band selection strategy in reproducing kernel Hilbert spaces, resulting in a complexity reduction of two orders of magnitude without performance loss.
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.