Iterative compressive sampling for hyperspectral images via source separation
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.