Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing

Hyperspectral imaging is an active research area in remote sensing. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. In this paper, we introduce a new compressed sensing methodology, termed Hyperspectral coded aperture (HYCA), which exploits the high correlation existing among the components of remotely sensed hyperspectral data sets to reduce the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images: the spectral vectors live on a low dimensional subspace and the spectral bands are piecewise smooth. The former property allows to represent the data vectors using a small number of coordinates, and the latter implies that each coordinate is piecewise smooth and thus compressible on local differences. The reconstruction of the data cube is obtained by minimizing a convex objective function containing a data term associated to the compressed measurements and a total variation spatial regularizer. A series of experiments with simulated and real data show the effectiveness of the newly developed HYCA, indicating that the proposed scheme has a high potential in real-world applications.