GPU implementation of spatial preprocessing for spectral unmixing of hyperspectral data

The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmem-bers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidia^TMGeForce GTX 580 and NVidia^TMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.