基于线性混合模型的高光谱图像谱间压缩感知重构  

Reconstruction of Hyperspectral Images with Spectral Compressive Sensing Based on Linear Mixing Models

作　　者：王忠良[1,2] 冯燕[1] 贾应彪[1] 

机构地区：[1]西北工业大学电子信息学院,西安710129 [2]铜陵学院电气工程系,铜陵241000

出　　处：《电子与信息学报》2014年第11期2737-2743,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61071171);安徽省高等学校省级自然科学研究项目(KJ2013B298)资助课题

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。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.

关 键 词：高光谱图像 压缩感知 感知矩阵 线性混合模型 

分 类 号：TP751.2[自动化与计算机技术—检测技术与自动化装置]