Hyper-spectral Image Super-resolution Using Non-negative Spectral Representation with Data-Guided Sparsity
Hyper-spectral Image Super-resolution Using Non-negative Spectral Representation with Data-Guided Sparsity
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DOI：
10.1109/ism.2017.99
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
X. Han;Jan Wang;Boxin Shi;Yinqiang Zheng;Yenwei Chen
中科院分区：
文献类型：
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作者：
X. Han;Jan Wang;Boxin Shi;Yinqiang Zheng;Yenwei Chen
高光谱成像在从遥感到医学成像的许多应用中具有了解不同材料特性的巨大潜力。然而，由于各种硬件限制，使用现有成像技术只能获得低分辨率高光谱和高分辨率多光谱图像。本研究旨在通过融合现有的LR-HS和HR-MS图像来生成高分辨率的高光谱图像。提出了一种新的基于自适应稀疏约束的反射光谱非负稀疏表示的高光谱图像超分辨率方法。通过分析现有高分辨率多光谱图像中聚焦像素的局部内容相似性，可以根据周围像素来衡量像素的材料纯度，生成稀疏图来指导光谱表示的非负稀疏编码优化过程，称为数据指导稀疏的非负光谱表示。该方法根据高分辨率图像的局部内容自适应地调整谱表示的稀疏性，从而能够产生更稳健的谱表示，用于目标高分辨率高光谱图像的恢复。在两个公开的高光谱数据集上进行的实验表明，与现有的方法相比，该方法具有良好的性能。
Hyperspectral imaging has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to various hardware limitations, only low-resolution hyperspectral and high-resolution multi-spectral images can be available using existing imaging techniques. This study aims to generate a high-resolution hyperspectral image via fusion of the available LR-HS and HR-MS images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectral with adaptive sparsity constraint. By analyzing local content similarity of a focused pixel in the available high-resolution multi-spectral image, which can measure pixel material purity according to surrounding pixels, we generate a sparsity map for guiding non-negative sparse coding optimization procedure of the spectral representation called non-negative spectral representation with data-guided sparsity. Since the proposed method adaptively adjust the sparsity in the spectral representation based on the local content of the available high-resolution multi-spectral image, it can produce more robust spectral representation for recovering the target high-resolution hyper-spectral image. Comprehensive experiments on two public hyperspectral datasets validate that the proposed method achieves promising performances compared with the existing state of the art methods.