谱间和帧内协同稀疏的高光谱图像压缩感知模型  

Hyperspectral image compressed sensing model based on the collaborative sparsity of the intra-frame and inter-band

作　　者：王相海[1,2] 宋海鹰[1] 宋传鸣[1] 陶兢喆[2] 

机构地区：[1]辽宁师范大学计算机与信息技术学院,大连116029 [2]辽宁师范大学自然地理与空间信息科学辽宁重点实验室,大连116029

出　　处：《中国科学：信息科学》2016年第3期361-375,共15页Scientia Sinica(Informationis)

基　　金：国家自然科学基金项目(批准号:41271422;61402214);高等学校博士学科点专项科研基金项目(批准号:20132136110002);辽宁省博士科研启动基金项目(批准号:20121076);辽宁省教育厅科学研究一般项目(批准号:L2013405;L2013406)资助

摘　　要：本文基于压缩感知理论,利用高光谱图像谱间存在极强的相关性,提出一种基于谱间和帧内协同稀疏的高光谱图像压缩感知模型(hyperspectral image collaborative sparsity measure,HICoSM).模型包括对高光谱图像各波段帧内及其帧间3方面的稀疏性挖掘:一是对各波段帧基于局部区域平滑性的稀疏性度量;二是对各波段帧基于非局部区域纹理、边缘等细节信息的自相似性稀疏性度量;三是相邻波段帧基于谱间相关性的预测稀疏性度量,具体利用前一个波段帧,通过最小二乘法线性预测形成当前波段帧的预测帧,通过确定预测帧与当前波段帧的最佳预测差实现谱间的稀疏性度量.进一步,给出了所提出模型的数值计算过程.仿真实验表明,模型HICoSM在对各个波段帧的稀疏性进行度量的基础上,通过挖掘和测量高光谱图像波段间的谱间稀疏性,有效地提高了各波段帧在压缩感知恢复阶段的解码质量.This paper proposes a new hyperspectral image compressed sensing model（hyperspectral image collaborative sparsity measure,HICoSM） based on the collaborative sparsity of the intra-frame and inter-band,which utilizes the strong correlation of the inter-band.The sparsity mining of the proposed model includes three aspects：the local smoothness of the intra-frame;the nonlocal self-similarity of the inter-band that measures the detail information of the texture and the edge;and the correlation prediction of the inter-band,which specifically utilizes the prior band via the least squares linear prediction method to predict the current band,and then calculates the best predicting residual to realize the inter-band sparsity measurement.Further,we describe the numerical procedure of the proposed model.A large number of simulation experiments show that the HICoSM can make an effective sparsity measurement for all the intra-frames.Simultaneously,it can sufficiently mine and measure the sparsity of the inter-band of a hyperspectral image and effectively improve the decoding quality of each band in the CS recovery stage.

关 键 词：高光谱图像 压缩感知 协同稀疏 谱间相关性 优化求解 预测残差 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]