Understanding image fusion

By Yun Zhang. 2004

540 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2464833b55a6725f3623bb9696f0d4534d70a80a

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Citations in the corpus

Fast and Robust Matching for Multimodal Remote Sensing Image Registration (2019, background): “Strictly speaking, this kernel is not a 3D Gaussian function in 3D space, which is composed of a 2D Gaussian kernel in X- and Y-directions and a kernel of [1,2,1] in the gradient orientation direction (hereinafter referred to as Z-direction).”
Remote sensing image fusion using the curvelet transform (2007, background): “True-color visualization has been deliberately chosen, because Pan-sharpening of MS bands falling partly outside the bandwidth of Pan, as in the case of the blue band B1, is particularly critical [32].”
Remote sensing imagery in vegetation mapping: a review (2008, background): “There are many studies focusing on the development of new fusion algorithms (Amarsaikhan and Douglas 2004; Zhang 2004; Zhu and Tateishi 2006).”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “A wide variety of image fusion methods have been proposed in the remote sensing literature [7], [17], [18], [38], [39], being pan-sharpening methods [24], [32], [35], [40] among the most widespread.”
A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter (2006, methodology): “In addition, a well-fused image gives a visually beautiful color image, especially for visualization purposes [2].”
A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement (2011, background): “With regard to remote sensing, it has been a difficult task to obtain highspatial-resolution MS images because of the technical limitations of satellite sensors, the incoming radiation energy to the sensor, and the data volume handled by the sensors [1].”
A review of remote sensing image fusion methods (2016, background): “It could mean improvement in further processing [29], better visual appearance [30], or enhancement in visual interpretation of data [31].”
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest (2007, background): “Generally speaking, if the spectral responses of the MS channels are not perfectly overlapped with the bandwidth of Pan, as it happens with the most advanced very high resolution imagers, Ikonos and QuickBird, IHS-based methods may yield poor results in terms of spectral ﬁdelity [15].”
Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing (2017, methodology): “Numerous pan-sharpening algorithms have been proposed in the past two decades (Pohl and van Genderen 1998, 2013, Zhang 2004, 2010, Ehlers et al. 2010, Jiang et al. 2014, Yang and Zhang 2014, Shen et al. 2015).”
Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data (2007, background): “yield poor results in terms of spectral fidelity [11].”