Reconstructing and segmenting hyperspectral images from compressed measurements

A joint reconstruction and segmentation model for hyperspectral data obtained from a compressive measurement system is proposed, and some preliminary tests are described. Although hyperspectral imaging (HSI) technology has incredible potential, its utility is currently limited because of the quantity and complexity of the data it gathers. Yet, often the scene to be reconstructed from the HSI data contains far less information, typically consisting of spectrally and spatially homogeneous segments that can be represented sparsely in an appropriate basis. Such vast informational redundancy thus implicitly contained in the HSI data warrants a compressed sensing (CS) strategy that acquires appropriately coded spectral-spatial data from which one can reconstruct the original image more efficiently, while still enabling target identification procedures. A coded-aperture snapshot spectral imager (CASSI) is considered here, and a joint reconstruction and segmentation model for data obtained from CASSI compressive measurements is proposed and preliminary numerical experiments are presented.