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摘要： 压缩感知理论利用目标的稀疏特性,能从极少的测量值中重构出目标图像,已成为突破奈奎斯特采样定理,实现超分辨成像的一个极具潜力的研究方向,其应用于对地观测遥感成像的一个核心问题在于面对复杂的地物场景,如何探求有效的稀疏化表达方法。对于具有超高数据量的高光谱成像而言,充分利用波段间丰富的冗余光谱信息,研究有效的光谱稀疏化表达方法更加具有实用价值。首先介绍了压缩感知光谱成像以及光谱稀疏化表达的基本原理,然后利用来自ASTER光谱库的多种类型地物光谱数据构建了一种基于K-SVD方法的训练字典,将其与DCT基、小波基分别作为稀疏基,对于几种典型地物目标进行仿真重构,结果表明:所构建的稀疏字典在采样数较少的情况下明显优于DCT基和小波基,在20%的低采样率时即可近乎完美地重构光谱曲线。 关键词： 压缩感知;  高光谱;  稀疏基;  字典训练     Abstract: Using object’s sparsity,compressive sensing theory is able to breakthrough Nyquist sampling theorem and reconstruct object image from very few observations,which has become a very potential research field to achieve super-resolution image.One of the crucial problems of applying compressive sensing to earth observation remote sensing image is how to pursue effective sparsification method under the complexity of ground scene.It will be more practical for hyperspectral imaging with a large amount of data to search for effective spectral sparsification method using rich redundant spectral information in bands.This paper introduces the basic principle of compressive sensing spectral imaging and spectral sparsification,and trains a redundant dictionary based on K\|SVD method using multiple types of ground objects’ spectral data from ASTER spectral library,reconstructs several typical objects using DCT basis,wavelet basis and training dictionary via simulation experiment and compares the reconstruction results.The result shows using training dictionary as sparse basis performs is better at low sampling rate than DCT basis and wavelet basis,spectral curves can be reconstructed almost perfectly at sampling rate as low as 20%.Our research indicates the great potential for sparse dictionary applied in compressive sensing spectral imaging.

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