Spatial Hessian Feature Guided Variational Model for Pan-Sharpening
Spatial Hessian Feature Guided Variational Model for Pan-Sharpening
复制标题
用于全色锐化的空间 Hessian 特征引导变分模型
DOI：
10.1109/tgrs.2015.2497966
复制
发表时间：
2015
影响因子：
8.2
通讯作者：
Naz Bushra
中科院分区：
文献类型：
--
作者：
Liu Pengfei;Xiao Liang;Zhang Jun;Naz Bushra
本文提出了一种新的空间海森特征引导的全色锐化变分模型，该模型旨在从低分辨率多光谱图像和高分辨率全色图像中获得具有高空间分辨率和光谱分辨率的全色图像。首先，我们假设低分辨率的MS图像对应于高分辨率全色锐化的MS图像的模糊和下采样版本。由于平移锐化的MS图像和平移图像是同一场景的两个图像，因此平移锐化的MS图像与平移图像具有相似的几何对应关系。为此，通过兴趣点检测来学习平移图像和平移锐化后的MS图像之间的几何对应关系作为空间位置一致性。其次，基于图像的空间Hessian特征，提出了一种新的矢量Hessian Frobenius范数项，用于约束全景图像和全景锐化MS图像之间的空间对应关系，以及全景锐化MS图像不同波段之间的内在联系。基于这些假设，提出了一种新的全息锐化变分模型。在此基础上，设计了一种在算子分裂框架下求解该模型的有效算法。最后，对模拟数据和实际数据的实验结果证明了该方法在产生高光谱质量和高空间质量的全色锐化结果方面的有效性。
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.