LAUSR

This article proposes a smart hyperspectral sensing strategy, implemented in the spectral domain, conceived for spaceborne sensor systems, where physical space, storage resources, and communication bandwidth are extremely scarce and expensive. Smart sensing means faster and hardware-friendly imaging. Instead of acquiring all band samples in the spectral domain, we randomly select a few band samples per spatial pixel location. A periodic structure of spectral band selector array (SBSA) is designed so that we can learn a subspace basis from subsamples, which is essential to the underlying hyperspectral image (HSI) recovery algorithm. This spectral subsampling sensing strategy yields a demosaicing problem. We propose a blind hyperspectral reconstruction technique termed hyperspectral demosaicing (Hy-demosaicing) exploiting spectral low-rankness and spatial correlation of HSIs. It is blind in the sense that the signal subspace is learned from measured spectral subsamples. The subspace basis is data-adaptive and provides a more compact representation than other non-adaptive representations. This adaptiveness leads to improved image recovery as illustrated in experiments with real data.