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A Robust Pan‑Sharpening Scheme for Improving Resolution of Satellite Images in the Domain of the Nonsubsampled Shearlet Transform

机译：一种坚固的平移方案，用于改善非法均采样的剪柏变换域中的卫星图像分辨率

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摘要

Pan-sharpening is a substantial post-processing task for captured multispectral remotely sensed satellite imagery. Its main purpose is to fuse the high spectral characteristics of the multispectral (MS) images with the high spatial information of the panchromatic (Pan) image to output a sharper MS image (pan-sharpened) that encompasses higher spectral and spatial resolutions. In this paper, we investigate a conception of a new pan-sharpening scheme using the pulse coupled neural network (PCNN) in the nonsubsampled shearlet transform (NSST) domain. This can be done based on two main steps. In the first step, the input MS and Pan images are individually decomposed into multi-scaled and multi-directional coefficients by NSST. Second, the PCNN is applied to the low-frequency coefficients, which are merged by a weighted firing energy fusion rule utilizing the PCNN firing times. The detail coefficients with higher matching value are chosen to be the fused detail coefficients. Lastly, the pan-sharpened image is generated by the inverse NSST. WorldView-2, GeoEye-1, and QuickBird satellite datasets are employed in the experiments which demonstrate that the investigated scheme gained the ability in preserving both the high spatial details and high spectral characteristics simultaneously without involving abundant computation time. In addition, various image quality metrics such as CC, RMSE, RASE, ERGAS, SAM, Q4, QNR, and SCC are adopted to assess the spectral and spatial qualities of the pan-sharpened image. The experimental results and performance analysis illustrated that our scheme improved performance efficiency and achieved superiority over other conventional techniques.
机译：泛锐是捕获多光谱偏心感测卫星图像的实质后处理任务。其主要目的是使多光谱（MS）图像的高光谱特性与平面（PAN）图像的高空间信息融合，以输出包含更高的光谱和空间分辨率的锐利MS图像（泛尖锐）。在本文中，我们研究了使用非脉冲耦合神经网络（PCNN）在非管制的Shearlet变换（NSST）域中的新泛锐化方案的概念。这可以根据两个主要步骤完成。在第一步中，输入MS和PAN​​图像通过NSST分别分解成多缩放和多向系数。其次，PCNN被应用于低频系数，其通过利用PCNN射击时间来由加权发射能量融合规则合并。选择具有更高匹配值的细节系数是融合细节系数。最后，泛尖的图像由逆NSST产生。 WorldView-2，Geoeye-1和Quickbird卫星数据集在实验中使用，证明研究方案在不涉及丰富计算时间的情况下同时保留高空间细节和高光谱特性的能力。另外，采用各种图像质量指标，例如CC，RMSE，RASE，ERGAS，SAM，Q4，QNR和SCC来评估泛尖锐图像的光谱和空间质量。实验结果和性能分析表明，我们的方案提高了性能效率并在其他常规技术上实现了优势。

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