A Variational Approach for Pan-Sharpening
A Variational Approach for Pan-Sharpening
复制标题
全色锐化的变分方法
DOI：
10.1109/tip.2013.2258355
复制
发表时间：
2013-07-01
影响因子：
10.6
通讯作者：
Zhang, Guixu
中科院分区：
文献类型：
--
作者：
Fang, Faming;Li, Fang;Zhang, Guixu
全色锐化是通过将低分辨率 MS 图像与相应的高分辨率全色 (PAN) 图像组合来获取高分辨率多光谱 (MS) 图像的过程。在本文中，我们基于三个基本假设提出了一种新的变分全色锐化方法：1）PAN图像的梯度可以是全色锐化图像波段梯度的线性组合； 2) 上采样的低分辨率 MS 图像可能是全色锐化图像的降级形式； 3）全色锐化图像谱方向的梯度应近似于上采样低分辨率MS图像的谱方向梯度。基于这些假设构建了能量泛函，其最小化器与最佳全色锐化结果相关。我们讨论了能量最小化的存在性，并描述了基于分裂 Bregman 算法的数值过程。为了验证我们方法的有效性，我们使用 QuickBird 和 IKONOS 数据将其与一些最先进的方案进行定性和定量比较。特别是，我们将现有的量化指标分为四类，并在每一类中选择两个代表进行更合理的量化评估。结果证明了我们的方法在相关评估基准方面的有效性和稳定性。此外，与其他变分方法的计算效率比较也表明我们的方法是显着的。
Pan-sharpening is a process of acquiring a high resolution multispectral (MS) image by combining a low resolution MS image with a corresponding high resolution panchromatic (PAN) image. In this paper, we propose a new variational pan-sharpening method based on three basic assumptions: 1) the gradient of PAN image could be a linear combination of those of the pan-sharpened image bands; 2) the upsampled low resolution MS image could be a degraded form of the pan-sharpened image; and 3) the gradient in the spectrum direction of pan-sharpened image should be approximated to those of the upsampled low resolution MS image. An energy functional, whose minimizer is related to the best pan-sharpened result, is built based on these assumptions. We discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. To verify the effectiveness of our method, we qualitatively and quantitatively compare it with some state-of-the-art schemes using QuickBird and IKONOS data. Particularly, we classify the existing quantitative measures into four categories and choose two representatives in each category for more reasonable quantitative evaluation. The results demonstrate the effectiveness and stability of our method in terms of the related evaluation benchmarks. Besides, the computation efficiency comparison with other variational methods also shows that our method is remarkable.