Complexity reduction by convex cone detection for unmixing hyperspectral images of bacterial biosensors

We address the problem of complexity reduction in hyperspectral image unmixing. When the hyperspectral images are highly resoluted, we propose to select a limited number of pixels, therefore reducing dramatically the size of the data. Then, the related mixtures are used as inputs to a positive source separation algorithm. Our pixel selection procedure is based on a convex cone analysis of the data mixtures; indeed, positive mixtures of sources are embedded in a convex cone whose boundary contains complete available information regarding the sources. We search for the least number of mixtures embedding the convex cone and then store the corresponding pixel indices as the selected pixels. We apply this method to the analysis of hyperspectral images of bacterial cells obtained on a confocal microscope. The bacterial cells, acting as whole-cell biosensors, display great potential as living transducers in sensing applications.