Lossy Hyperspectral Image Compression Tuned for Spectral Mixture Analysis Applications on NVidia? Graphics Processing Units

摘要

In this paper, we develop a computationally efficient approach for lossy compression of remotely sensed hyperspectral images which has been specifically tuned to preserve the relevant information required in spectral mixture analysis (SMA) applications. The proposed method is based on two steps: 1) endmember extraction, and 2) linear spectral unmixing. Two endmember extraction algorithms: the pixel purity index (PPI) and the automatic morphological endmember extraction (AMEE), and a fully constrained linear spectral unmixing (FCLSU) algorithm have been considered in this work to devise the proposed lossy compression strategy. The proposed methodology has been implemented in graphics processing units (GPUs) of NVidia? type. Our experiments demonstrate that it can achieve very high compression ratios when applied to standard hyperspectral data sets, and can also retain the relevant information required for spectral unmixing in a computationally efficient way, achieving speedups in the order of 26 on a NVidia? GeForce 8800 GTX graphic card when compared to an optimized implementation of the same code in a dual-core CPU.