August Yitzhak, Vachman Chaim, Rivenson Yair, Stern Adrian
Department of Electro-Optical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Appl Opt. 2013 Apr 1;52(10):D46-54. doi: 10.1364/AO.52.000D46.
An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.
提出了一种用于高光谱数据压缩感知的高效方法和系统。通过对高光谱数据立方体的空间和光谱域进行随机编码来实现压缩效率。采用可分离传感架构来降低与大量数据的压缩感知相关的计算复杂度，这在高光谱成像中很典型。该系统能够优化空间和光谱压缩感知率之间的比率。通过对真实高光谱数据进行的模拟验证了该方法。
