中文摘要：

该文针对高光谱数据的线性混合模型，提出一种简单有效的谱间压缩感知下高光谱数据的重构方案。该方案不同于传统的压缩感知重构方法直接重构高光谱数据，而是将高光谱数据分离成端元和丰度分别进行重构，然后利用重构的端元和丰度信息合成高光谱数据。实验结果表明，该方案的重构质量明显优于标准压缩感知重构方法，并且运算速度具有极大提升，同时便于获得端元和丰度信息。

英文摘要：

A simple and effective reconstruction scheme of hyperspectral data with spectral Compressive Sensing （CS） is proposed based on the widely used linear mixing model. The scheme is different from the traditional reconstruction methods of compressive sensing, which reconstruct hyperspectral data directly. The proposed scheme separates hyperspectral data into endmembers and abundances to reconstruct respectively, then generates hyperspectral data by reconstructed endmembers and abundances. Experimental results show that the reconstruction quality of the proposed scheme is better than the standard compressive sensing, furthermore the computing speed greatly ascends. Simultaneously, as a byproduct, endmembers and abundances can be obtained directly.