Hyperspectral compressive sensing from spectral projections
Hyperspectral data compression has received considerable interest in recent years. Contrarily to the conventional compression schemes, which first acquire the full data set and then implement some compressing technique, compressive sensing (CS) acquires directly the compressed signal which will be later recovered on the ground station. The CS paradigm fits perfectly the requirements of onborad hyperspectral imaging systems in terms of energy, computing power, and bandwidth. By using CS in these systems, the amount of data acquired and transmitted to the ground stations is reduced and the bulk of the computation to infer the original data is carried out in the ground stations. In this paper, we present a new technique to perform CS of hyperspectral images (HSIs), which exploit the fact that HSIs admits a low dimensional linear representation. The proposed method is blind in the sense that linear representation is learned with low computational cost from the compressed measurements. Furthermore the proposed method is very light from the computational point of view and it can recover perfectly the original image in noise-free scenarios. The effectiveness of the proposed method is illustrated in both synthetic and real scenarios.