Robust and Effective Hyperspectral Pansharpening Using Spatio-Spectral Total Variation
Robust and Effective Hyperspectral Pansharpening Using Spatio-Spectral Total Variation
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DOI：
10.1109/icassp.2018.8462464
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Saori Takeyama;Shunsuke Ono;I. Kumazawa
中科院分区：
文献类型：
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作者：
Saori Takeyama;Shunsuke Ono;I. Kumazawa
获取高分辨率高光谱图像是一项非常具有挑战性的任务。为此，已经广泛地研究了高光谱全色锐化技术，其从高光谱分辨率但低空间分辨率的HS图像（低HS图像）和高空间分辨率全色（PAN）图像的对估计高空间和光谱分辨率的HS图像（高HS图像）。然而，由于这些方法在估计中没有充分利用HS图像上的光谱信息的分段平滑性，当低HS图像包含噪声时，它们往往产生光谱失真。为了解决这个问题，我们提出了一种新的高光谱泛锐化方法，使用空间光谱正则化。我们的方法不仅有效地利用观测信息，但也适当地促进了空间-光谱分段平滑的高HS图像，导致高质量和鲁棒的估计。所提出的方法被减少到一个非光滑凸优化问题，这是有效地解决了原始-对偶分裂方法。我们的实验表明，我们的方法比现有的高光谱泛锐化方法的优势。
Acquiring high-resolution hyperspectral (HS) images is a very challenging task. To this end, hyperspectral pansharpening techniques have been widely studied, which estimate an HS image of high spatial and spectral resolution (high HS image) from a pair of an HS image of high spectral resolution but low spatial resolution (low HS image) and a high spatial resolution panchromatic (PAN) image. However, since these methods do not fully utilize the piecewise-smoothness of spectral information on HS images in estimation, they tend to produce spectral distortion when the low HS image contains noise. To tackle this issue, we propose a new hyperspectral pansharpening method using a spatio-spectral regularization. Our method not only effectively exploits observed information but also properly promotes the spatio-spectral piecewise-smoothness of the resulting high HS image, leading to high quality and robust estimation. The proposed method is reduced to a nonsmooth convex optimization problem, which is efficiently solved by a primal-dual splitting method. Our experiments demonstrate the advantages of our method over existing hyperspectral pansharpening methods.