4 March 2014 Iterative compressive sampling for hyperspectral images via source separation
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Compressive Sensing (CS) is receiving increasing attention as a way to lower storage and compression requirements for on-board acquisition of remote-sensing images. In the case of multi- and hyperspectral images, however, exploiting the spectral correlation poses severe computational problems. Yet, exploiting such a correlation would provide significantly better performance in terms of reconstruction quality. In this paper, we build on a recently proposed 2D CS scheme based on blind source separation to develop a computationally simple, yet accurate, prediction-based scheme for acquisition and iterative reconstruction of hyperspectral images in a CS setting. Preliminary experiments carried out on different hyperspectral images show that our approach yields a dramatic reduction of computational time while ensuring reconstruction performance similar to those of much more complicated 3D reconstruction schemes.
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S. Kamdem Kuiteing, S. Kamdem Kuiteing, Mauro Barni, Mauro Barni, "Iterative compressive sampling for hyperspectral images via source separation", Proc. SPIE 9022, Image Sensors and Imaging Systems 2014, 90220T (4 March 2014); doi: 10.1117/12.2037794; https://doi.org/10.1117/12.2037794


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