Hyperspectral compressive acquisition in the spatial domain via blind factorization
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; the original signal is later recovered by solving a suitable optimization problem. 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 data acquired in the system 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 relies in two characteristics of these data: a) HSIs admits a low dimensional linear representation; and b) the coefficients (in the spatial domain) of the linear representations are are piecewise smooth, thus compressible. The proposed method is blind in the sense that linear representation is learned with low computational cost from the compressed measurements. The effectiveness of the proposed CS method is illustrated in both synthetic and real scenarios.