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4 March 2014 Iterative compressive sampling for hyperspectral images via source separation
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Proceedings Volume 9022, Image Sensors and Imaging Systems 2014; 90220T (2014)
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
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 and Mauro Barni "Iterative compressive sampling for hyperspectral images via source separation", Proc. SPIE 9022, Image Sensors and Imaging Systems 2014, 90220T (4 March 2014);

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