A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors
A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors
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DOI：
10.1109/tip.2014.2333661
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发表时间：
2014-09-01
影响因子：
10.6
通讯作者：
Xia, Junshi
中科院分区：
文献类型：
--
作者：
He, Xiyan;Condat, Laurent;Xia, Junshi
近年来，多传感器系统的发展导致了可用遥感数据量的大幅增加。图像融合技术旨在从多个传感器获得的同一区域的退化版本中推断出给定区域的高质量图像。本文重点研究全色锐化，这是一个高空间分辨率的多光谱图像的推理从两个退化的版本具有互补的光谱和空间分辨率特性：1）低空间分辨率的多光谱图像和2）高空间分辨率全色图像。我们引入了一个新的变分模型的基础上的空间和光谱稀疏先验的融合。在谱域中，我们鼓励低秩结构，而在空间域中，我们促进局部差异的稀疏性。由于全色和多光谱图像是使用不同的通道响应的基础连续光谱的集成，我们建议利用适当的正则化的基础上全色和融合多光谱图像之间的空间和光谱链接。采用数据矩阵的向量全变差范数的加权版本来将融合图像的空间信息与全色图像的空间信息对准。关于光谱信息，提出了两种不同类型的正则化，以促进全色和融合多光谱图像之间的线性依赖的软约束。第一个估计直接从所观察到的全色和低分辨率多光谱图像的线性回归系数，而第二个采用主成分追求，以获得一个强大的恢复底层的低秩结构。我们还证明了这两个正则化子是强相关的。这两种正则化器的基本思想是融合图像应该具有低秩并保持边缘位置。我们使用最近提出的分裂增广拉格朗日收缩算法的变化，以有效地解决所提出的变分制剂。仿真和真实的遥感图像的实验结果表明，与现有技术相比，该方法是有效的。
The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: 1) a low spatial resolution multispectral image and 2) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain, we encourage low-rank structure, whereas in the spatial domain, we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and fused multispectral images. A weighted version of the vector total variation norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low-resolution multispectral images by linear regression while the second one employs the principal component pursuit to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed split augmented Lagrangian shrinkage algorithm to effectively solve the proposed variational formulations. Experimental results on simulated and real remote sensing images show the effectiveness of the proposed pansharpening method compared with the state-of-the-art.