Block compressive sensing of hyperspectral images based on prediction error

The paper proposed the new hyperspectral image compression algorithm by using block compressive sensing and inter-band prediction error. Because of making the best use of the intense spectral correlation between adjacent bands, we can obtain the higher reconstruction quality and lower complexity. In this method, we firstly estimate the prediction parameters between adjacent bands by using least squares method. Then each band is measured by the same random projection matrix independently. Then the prediction parameters and random measurements are transmitted to the decoder. At last, we exploit a new restore process and prediction information to reconstruct the hyperspectral images. The prediction information is computed based on the previous restore adjacent band and the prediction parameters. Several experimental results demonstrate that the proposed algorithm is obviously better than the traditional compressive sensing, and has a very low complexity. In addition, our algorithm is easy to realize in practice.