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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Combining Component Substitution and Multiresolution Analysis: A Novel Generalized BDSD Pansharpening Algorithm
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Combining Component Substitution and Multiresolution Analysis: A Novel Generalized BDSD Pansharpening Algorithm

机译:结合组件替换和多分辨率分析:一种新型的BDSD泛锐化算法

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摘要

Modern optical satellites can acquire bundles of panchSromatic (PAN) and multispectral (MS) images of the scene simultaneously. Because of the complexity of the sensors and amount of data involved, an MS image always has lower spatial resolution than the corresponding PAN image. Pansharpening aims at fusing MS images and PAN images, characterized by the spectral content of the former and the spatial details of the latter. There are two main large families of pansharpening algorithms, i.e., component substitution (CS) and multiresolution analysis (MRA). Generally speaking, the CS algorithms have better performance on spatial detail injection, while the MRA shows better spectral content preservation. In this paper, we propose a novel pansharpening algorithm, which combines the conceptions of CS and MRA. This proposed algorithm can be regarded as a generalized version of the existing band-dependent spatial-detail (BDSD) algorithm. A semisimulated dataset and three real datasets are adopted to compare the performance among the generalized-BDSD algorithm and six existing popular pansharpening algorithms. It shows that the proposed method has much lower spectral distortion and good visual appearance. In other words, the proposed method aggregates the advantages of CS and MRA, which shows effectiveness in practice.
机译:现代光学卫星可以同时获取场景的panchSromatic(PAN)和多光谱(MS)图像束。由于传感器的复杂性和涉及的数据量,MS图像始终比相应的PAN图像具有较低的空间分辨率。 Pansharpening旨在融合MS图像和PAN图像,其特征在于前者的光谱内容和后者的空间细节。全屏锐化算法主要有两个大家族,即组件替换(CS)和多分辨率分析(MRA)。一般来说,CS算法在空间细节注入方面具有更好的性能,而MRA显示出更好的频谱内容保留。在本文中,我们提出了一种新颖的泛锐化算法,该算法结合了CS和MRA的概念。可以将该提议算法视为现有的带相关空间细节(BDSD)算法的广义版本。采用半模拟数据集和三个真实数据集来比较广义BDSD算法和六种现有流行的全锐化算法的性能。结果表明,所提出的方法具有较低的光谱畸变和良好的视觉外观。换句话说,该方法融合了CS和MRA的优点,在实践中显示出了有效性。

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