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Spatio-Spectral Image Fusion Using Local Embeddings

机译：使用局部嵌入的时空光谱图像融合

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Image fusion is extensively used in remote sensing to combine multiple images into a more informative single image, which is more suitable for human and machine perception. The purpose of image fusion algorithms is to achieve a single image with high spatial resolution and high spectral resolution. The major challenges faced by existing algorithms are of output image quality in terms of colour distortion and blurring. This paper models the spatio-spectral image fusion as a problem of non-linear dimensionality reduction. The aim is to define each point of fused image as a linear combination of its neighbours in multi spectral image and the corresponding pixel values from down scaled panchromatic image. The assumption here is that the spectral behaviour of fused image is similar to that of multispectral image. The performance of the model is compared with existing state of the art methods for same-sensor dataset.
机译：图像融合被广泛用于遥感中，以将多个图像组合成一个信息量更大的单个图像，这更适合于人和机器的感知。图像融合算法的目的是获得具有高空间分辨率和高光谱分辨率的单个图像。现有算法面临的主要挑战是在颜色失真和模糊方面的输出图像质量。本文将时空光谱图像融合建模为非线性降维问题。目的是将融合图像的每个点定义为多光谱图像中其邻居与缩小的全色图像中相应像素值的线性组合。这里的假设是，融合图像的光谱行为类似于多光谱图像。将模型的性能与相同传感器数据集的现有技术水平的现有方法进行比较。

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