Abstract: Compressed sensing has proposed a new mechanism for data acquisition and compression, which can shift heavy computational loads from encoders to decoders. The hyperspectral images have sparse/compressible representations on some orthonormal bases and are of spectral and spatial correlations. According to the prior information of hyperspectral images,a novel hyperspectral compressed sensing projection and reconstruction method via compound regularizers is proposed. At the encoder, it only needs a simple projection. In the Implementation of the reconstruction algorithm, the problem of compound regularizers is turned into dealing with a few simple optimization problems by applying the variable-splitting method and is solved by iteration. Experimental results show that the proposed algorithm is able to reconstruct the hyperspectral images more efficiently than the current algorithms. Our method has very low decoding complexity and it is suitable for severely resource-constrained spaceborne and airborne remote sensing platforms.

Key words: compressed sensing, hyperspectral images, data compression, measurement, reconstruction, convex optimization