He Xiyan, Condat Laurent, Bioucas-Diaz Jose, Chanussot Jocelyn, Xia Junshi
IEEE Trans Image Process. 2014 Sep;23(9):4160-4174. doi: 10.1109/TIP.2014.2333661. Epub 2014 Jun 27.
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: a) a low spatial resolution multispectral image; and b) 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 the fused multispectral images. A weighted version of the vector Total Variation (TV) 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 the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) 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 (SALSA) 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 to the state-of-the-art.
近年来多传感器系统的发展使得可用遥感数据量大幅增加。图像融合技术旨在从多个传感器获取的同一区域的退化版本中推断出给定区域的高质量图像。本文重点关注全色锐化，即从具有互补光谱和空间分辨率特征的两个退化版本中推断出高空间分辨率多光谱图像：a）低空间分辨率多光谱图像；b）高空间分辨率全色图像。我们引入了一种基于空间和光谱稀疏先验的新变分模型用于融合。在光谱域，我们鼓励低秩结构，而在空间域，我们促进局部差异的稀疏性。鉴于全色图像和多光谱图像都是使用不同通道响应的基础连续光谱的积分，我们建议利用基于全色图像与融合后的多光谱图像之间空间和光谱联系的适当正则化。采用数据矩阵向量总变差（TV）范数的加权版本使融合图像的空间信息与全色图像的空间信息对齐。关于光谱信息，提出了两种不同类型的正则化，以促进对全色图像与融合后的多光谱图像之间线性相关性的软约束。第一种通过线性回归（LR）直接从观测到的全色图像和低分辨率多光谱图像估计线性系数，而第二种采用主成分追踪（PCP）来稳健恢复基础低秩结构。我们还表明这两种正则化密切相关。两种正则化的基本思想是融合后的图像应具有低秩并保留边缘位置。我们使用最近提出的分裂增广拉格朗日收缩（SALSA）算法的变体来有效求解所提出的变分公式。在模拟和真实遥感图像上的实验结果表明，与现有技术相比，所提出的全色锐化方法是有效的。
