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An Improved Pansharpening Method for Misaligned Panchromatic and Multispectral Data

机译:一种改进的解体化的全音和多光谱数据的平移方法

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摘要

Numerous pansharpening methods were proposed in recent decades for fusing low-spatial-resolution multispectral (MS) images with high-spatial-resolution (HSR) panchromatic (PAN) bands to produce fused HSR MS images, which are widely used in various remote sensing tasks. The effect of misregistration between MS and PAN bands on quality of fused products has gained much attention in recent years. An improved method for misaligned MS and PAN imagery is proposed, through two improvements made on a previously published method named RMI (reduce misalignment impact). The performance of the proposed method was assessed by comparing with some outstanding fusion methods, such as adaptive Gram-Schmidt and generalized Laplacian pyramid. Experimental results show that the improved version can reduce spectral distortions of fused dark pixels and sharpen boundaries between different image objects, as well as obtain similar quality indexes with the original RMI method. In addition, the proposed method was evaluated with respect to its sensitivity to misalignments between MS and PAN bands. It is certified that the proposed method is more robust to misalignments between MS and PAN bands than the other methods.
机译:近几十年来提出了许多粉丝化方法,用于融合具有高空间分辨率(HSR)Panchromatic(PAN)频带的低空间分辨率多光谱(MS)图像以产生融合的HSR MS图像,这些耦合HSR MS图像广泛用于各种遥感任务。近年来,MS与PAN乐队在MS和PAN​​乐队之间的效果效益大幅关注。提出了一种改进的未对准MS和PAN​​图像的方法,通过两种名为RMI(减少未对准影响)的先前发表的方法进行了两种改进。通过与一些出色的融合方法进行评估,评估所提出的方法的性能,例如自适应革兰施密特和广义拉普拉斯金字塔。实验结果表明,改进的版本可以减少融合暗像素的光谱扭曲,并在不同图像对象之间锐化边界,以及通过原始RMI方法获得类似的质量指标。此外,通过对MS和PAN​​带之间的敏感性评估所提出的方法。它被证明是所提出的方法对MS与PAN带之间的错位比其他方法更加强大。

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