Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries
Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries
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DOI：
10.1109/tgrs.2012.2230332
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发表时间：
2013-02
影响因子：
8.2
通讯作者：
Shutao Li;Haitao Yin;Leyuan Fang
中科院分区：
文献类型：
--
作者：
Shutao Li;Haitao Yin;Leyuan Fang
遥感图像融合是将全色（PAN）图像的空间细节信息与低分辨率多光谱（MS）图像的光谱信息相结合，生成具有高空间分辨率的融合MS图像。提出了一种基于学习字典的稀疏表示的遥感图像融合方法。从源图像中自适应地学习PAN图像和低分辨率MS图像的字典。此外，设计了一种新的策略来构建未知的高分辨率MS图像的字典，没有训练集，这可以使我们提出的方法更实用。采用正交匹配追踪算法对PAN图像和低分辨率MS图像进行稀疏系数搜索。然后，通过将所获得的稀疏系数与用于高分辨率MS图像的字典相结合来计算融合的高分辨率MS图像。通过对QuickBird和IKONOS影像的仿真和真实的实验结果表明，该方法具有一定的优越性。
Remote sensing image fusion can integrate the spatial detail of panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MS) image to produce a fused MS image with high spatial resolution. In this paper, a remote sensing image fusion method is proposed with sparse representations over learned dictionaries. The dictionaries for PAN image and low-resolution MS image are learned from the source images adaptively. Furthermore, a novel strategy is designed to construct the dictionary for unknown high-resolution MS images without training set, which can make our proposed method more practical. The sparse coefficients of the PAN image and low-resolution MS image are sought by the orthogonal matching pursuit algorithm. Then, the fused high-resolution MS image is calculated by combining the obtained sparse coefficients and the dictionary for the high-resolution MS image. By comparing with six well-known methods in terms of several universal quality evaluation indexes with or without references, the simulated and real experimental results on QuickBird and IKONOS images demonstrate the superiority of our method.