高分辨率的应用需求使得传统的高光谱遥感成像系统面临高速率采样、海量数据存储等难以突破的瓶颈问题，压缩感知理论为传统高光谱遥感所面临的瓶颈问题提供了解决可能。针对高光谱压缩感知成像，提出了一种摆扫型高光谱压缩成像系统，该系统采用光栅、柱面透镜、二维编码孔径和线性传感阵列等光电器件，一次曝光中可获取空间像素点的光谱维向量对应的多个压缩采样值。在压缩感知数据重建过程中，为了充分利用高光谱图像的空间相关先验信息，提出了一种空间预测迭代重建算法。实验结果表明，与标准压缩感知重建算法对比，该算法在压缩感知采样率超过0.2时重建图像信噪比可提高10 dB 以上。所设计的系统简单易实现，可应用于星载、机载等遥感平台的高光谱压缩成像。

ool of Information Science and Engineering, Shaoguan University, Shaoguan 512005, China; 2.School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China) Abstract ： Owing to the requirements of high spectral resolution, conventional hyperspectral remote-sensing imaging systems are susceptible to bottleneck problems related to high rate sampling and mass data storage. Compressive sampling possesses the potential to solve many problems associated with hyperspectral remote sensing. An optical imaging system for compressive whiskbroom sensing in hyperspectral remote-sensing imaging is proposed in this paper. The proposed system comprises spatial grating, a cylindrical lens, a two-dimensional coded aperture, and a linear sensor array. The system, which enables multiple simultaneous compressive measurements, is designed for spectrum sensing operations. An iterative prediction reconstruction algorithm is designed based on the spatial correlation of hyperspectral images. Experimental results show that the reconstruction signal-to-noise ratio of the proposed algorithm is improved by more than 10 dB when the sampling rate exceeds 0.2. The sampling simplicity of the system makes it suitable for hyperspectral compressive imaging in space-borne and airborne remote-sensing platforms.