高光谱图像光谱维结构相关性及稀疏重建模型

Spectral dimensional correlation and sparse reconstruction model of hyperspectral images

高光谱图像(HSI)的高维度、高冗余等特性给其传输和处理带来了极大的挑战.近年来基于压缩感知的HSI重建受到重视并成为一前沿问题,在这一领域中有效挖掘HSI的稀疏先验成为提高其重建质量的一个关键.本文首先定义了HSI波段组的"光谱维面",并以此为切入点对HSI光谱维的结构相关性进行研究,获得了如下结论:一是HSI光谱维面的纹理分布较空间域更为简单和均匀,且其纹理的对比度低、平稳度高,更容易被稀疏表达,二是在HSI光谱维面,与参考块相邻的搜索区域内存在着一定数量的与参考块具有较高相似度的光谱曲线块;进一步确定了基于光谱维面的HSI光谱维结构相关性的涵义,并提出了相应的稀疏测量模型;在此基础上,通过整合空间维非局部相关性与光谱维结构相关性的稀疏表示,提出了稀疏模型S-SCo SM,并以其为稀疏约束先验构建了HSI的稀疏重构模型.大量实验表明,所提出的稀疏模型S-SCo SM从空间维和光谱维两个角度更深层次地挖掘了HSI的相关性,获得了更为充分和有效的HSI稀疏约束先验,使HSI的重构质量得到进一步提升,在有效提高重构波段图像空间信息质量的同时,很好地保持了波段组的光谱属性.

The high dimensionality and redundancy properties of hyperspectral image(HSI)constitute great challenges for transmission and process.Recently,HSI reconstruction based on compressed sensing has received increasing attention and has become a frontier problem.Effectively exploiting the sparse prior of HSI is crucial to improve the reconstruction quality.In this paper,the spectral dimension plane of the HSI band group is defined,and the structural correlation of HSI spectral dimension is studied.The following conclusions can be obtained.First,the texture distribution of the HSI spectral dimension is simpler and more uniform than that of the HIS spatial domain,and its texture exhibits low contrast and high properties,thus simplifying the sparse representation process.Second,in the HSI spectral dimension plane,the search area adjacent to the reference block has a certain number of spectral curve blocks that are highly similar to the reference block.Based on this observation,the structure correlation of the HIS spectral dimension is defined,and a sparse measurement model is proposed.Finally,the sparse model S-SCo SM is proposed by integrating the sparse representation of spatial dimension nonlocal correlation and the spectral dimension structure correlation.The sparse reconstruction model of HSI is constructed using this sparse constrain prior.Experimental results show that by further exploring the correlation of HSI from the viewpoint of spatial and spectral domains,the proposed sparse model S-SCo SM obtains a more adequate and effective HSI sparse constraint prior;hence,the reconstruction quality of HSI is improved.Consequently,the spatial information quality of the reconstructed band image can be effectively improved as well as the spectral attributes of band groups can be well maintained.