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A survey of pansharpening methods with a new band-decoupled variational model

机译:用新的带解耦变分模型研究整锐化方法

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Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components. This requirement is achieved by dropping the, commonly used, assumption that relates the spectral and panchromatic modalities by a linear transformation. Instead, a new constraint that preserves the radiometric ratio between the panchromatic and each spectral component is introduced. An exhaustive performance comparison of the proposed fusion method with several classical and state-of-the-art pansharpening techniques illustrates its superiority in preserving spatial details, reducing color distortions, and avoiding the creation of aliasing artifacts. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译:大多数卫星将以高空间分辨率获得的全色图像与以较低空间分辨率获得的多光谱图像分离。 Pansharpening是一种融合技术,用于提高多光谱数据的空间分辨率,同时保留其光谱信息。在本文中,我们将泛锐化视为具有非局部正则项的最小化成本函数的优化问题。对于每个频带,要最小化的能量功能会解耦,因此允许应用程序错误记录频谱分量。通过删除通常通过线性变换将光谱和全色模态关联起来的假设,可以实现此要求。取而代之的是,引入了一种新的约束条件,该约束条件保留了全色和每个光谱分量之间的辐射比。所提出的融合方法与几种经典的和最先进的全锐化技术的详尽性能比较表明,它在保留空间细节,减少颜色失真以及避免产生锯齿伪像方面具有优势。 (C)2016国际摄影测量与遥感学会(ISPRS)。由Elsevier B.V.发布。保留所有权利。

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