Lossy compression of hyperspectral images optimizing spectral unmixing

In this paper, we present a new hyperspectral image lossy compression method that aims to optimally compress in both spatial and spectral domains and simultaneously considers linear spectral unmixing as a target. To achieve this, a non-negative tucker decomposition is applied. This algorithm has three flexible dimension parameters. We propose an approach that, for any desired compression ratio (CR), chooses the optimal parameters by minimizing the root mean square error (RMSE) between the abundance matrices of the original and compressed datasets using fully constrained least square spectral unmixing. The resulting optimization problem is solved by a Particle Swarm Optimization algorithm. Our simulation results show that the proposed method, in comparison with well-known lossy compression methods such as 3D-SPECK and combined PCA+JPEG2000 algorithms, provides a lower RMSE and higher signal to noise ratio (SNR) for any given CR. It is noteworthy to mention that the superiority of our method becomes more apparent as the value of CR grows.