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Joint Sparse-Collaborative Representation to Fuse Hyperspectral and Multispectral Images
Signal Processing ( IF 4.4 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.sigpro.2020.107585
Changda Xing , Meiling Wang , Chong Dong , Chaowei Duan , Zhisheng Wang

Abstract Representation based methods to fuse a low spatial resolution hyperspectral image (LS-HSI) and a high spatial resolution multispectral image (HS-MSI) for reconstructing a high spatial resolution hyperspectral image (HS-HSI) have attracted increasing interest in recent years. Existing representation based algorithms only emphasize the sparsity of data, ignoring the collaboration, which may cause fusion performance degradation. In this paper, we develop a novel fusion method based on joint sparse-collaborative representation (SCR) for LS-HSI and HS-MSI. The SCR method consists of three steps: 1) sparse and collaborative dictionaries are learned to extract the spectral information of the given LS-HSI from two perspectives; 2) the turbopixel based segmentation is used for obtaining unfixed-size patches to describe the complex local structure of the HS-MSI; 3) the joint sparse-collaborative representation model is established for patch representing to reconstruct the HS-HSI. Compared with existing representation based strategies, the SCR not only considers the data sparsity, but also preserves the collaboration reflecting correlations among different spectral bands. In addition, the CSR more sufficiently utilizes the context of the given data, relying on unfixed-size patch dividing with adaptive adjustment by the turbopixel based segmentation. Experimental results indicate that the SCR achieves better performance than several state-of-the-art algorithms.

中文翻译:

融合高光谱和多光谱图像的联合稀疏协作表示

摘要 近年来,基于表征的融合低空间分辨率高光谱图像 (LS-HSI) 和高空间分辨率多光谱图像 (HS-MSI) 以重建高空间分辨率高光谱图像 (HS-HSI) 的方法引起了越来越多的兴趣。现有的基于表示的算法只强调数据的稀疏性,忽略了协作,这可能会导致融合性能下降。在本文中,我们为 LS-HSI 和 HS-MSI 开发了一种基于联合稀疏协作表示(SCR)的新型融合方法。SCR方法包括三个步骤:1)学习稀疏和协作字典,从两个角度提取给定LS-HSI的光谱信息;2)基于turbopixel的分割用于获得不固定大小的patch来描述HS-MSI的复杂局部结构;3)建立联合稀疏协同表示模型,用于块表示重建HS-HSI。与现有的基于表示的策略相比,SCR 不仅考虑了数据稀疏性,还保留了反映不同光谱带之间相关性的协作。此外,CSR 更充分地利用给定数据的上下文,依赖于不固定大小的补丁划分和基于涡轮像素的分割的自适应调整。实验结果表明,SCR 比几种最先进的算法实现了更好的性能。3)建立联合稀疏协同表示模型,用于块表示重建HS-HSI。与现有的基于表示的策略相比,SCR 不仅考虑了数据稀疏性,还保留了反映不同光谱带之间相关性的协作。此外,CSR 更充分地利用给定数据的上下文,依赖于不固定大小的补丁划分和基于涡轮像素的分割的自适应调整。实验结果表明,SCR 比几种最先进的算法实现了更好的性能。3)建立联合稀疏协同表示模型,用于块表示重建HS-HSI。与现有的基于表示的策略相比,SCR 不仅考虑了数据的稀疏性,而且还保留了反映不同光谱带之间相关性的协作。此外,CSR 更充分地利用给定数据的上下文,依赖于不固定大小的补丁划分和基于涡轮像素的分割的自适应调整。实验结果表明,SCR 比几种最先进的算法实现了更好的性能。CSR 更充分地利用给定数据的上下文,依赖于不固定大小的补丁划分,通过基于涡轮像素的分割进行自适应调整。实验结果表明,SCR 比几种最先进的算法实现了更好的性能。CSR 更充分地利用给定数据的上下文,依赖于不固定大小的补丁分割和基于涡轮像素的分割的自适应调整。实验结果表明,SCR 比几种最先进的算法实现了更好的性能。
更新日期:2020-08-01
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