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Spatial-Hessian-Feature-Guided Variational Model for Pan-Sharpening

机译：泛锐化的空间黑素特征指导变分模型

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摘要

In this paper, we propose a new spatial-Hessian-feature-guided variational model for pan-sharpening, which aims at obtaining a pan-sharpened multispectral (MS) image with both high spatial and spectral resolutions from a low-resolution MS image and a high-resolution panchromatic (PAN) image. First, we assume that the low-resolution MS image corresponds to the blurred and downsampled version of the high-resolution pan-sharpened MS image. Since the pan-sharpened MS image and the PAN image are two images of the same scene, the pan-sharpened MS image shares similar geometric correspondence with the PAN image. To this end, the geometric correspondence between the PAN image and the pan-sharpened MS image is learnt as spatial position consistency by interest point detection. Second, a new vectorial Hessian Frobenius norm term based on the image spatial Hessian feature is presented to constrain the special correspondence between the PAN image and the pan-sharpened MS image, as well as the intracorrelations among different bands of the pan-sharpened MS image. Based on these assumptions, a novel variational model is proposed for pan-sharpening. Accordingly, an efficient algorithm for the proposed model is designed under the operator splitting framework. Finally, the results on both simulated data and real data demonstrate the effectiveness of the proposed method in producing pan-sharpened results with high spectral quality and high spatial quality.
机译：在本文中，我们提出了一种新的由空间-黑森特征指导的全变分模型，旨在从低分辨率的MS图像和具有高空间分辨率和光谱分辨率的全变锐多光谱（MS）图像中获取图像。高分辨率全色（PAN）图像。首先，我们假设低分辨率MS图像对应于高分辨率全锐化MS图像的模糊和降采样版本。由于全脸锐化的MS图像和PAN图像是同一场景的两个图像，因此全脸锐化的MS图像与PAN图像具有相似的几何对应关系。为此，通过兴趣点检测将PAN图像和泛锐化的MS图像之间的几何对应关系学习为空间位置一致性。其次，提出了一种新的基于图像空间Hessian特征的矢量Hessian Frobenius范数项，以约束PAN图像与泛锐化MS图像之间的特殊对应关系，以及泛锐化MS图像不同波段之间的内相关。 。基于这些假设，提出了一种用于泛锐化的新型变分模型。因此，在算子拆分框架下设计了一种针对所提出模型的有效算法。最后，在模拟数据和真实数据上的结果都证明了该方法在产生具有高频谱质量和高空间质量的泛锐化结果中的有效性。

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