The nearest-neighbor diffusion-based algorithm (NNDiffuse) has seen great success in multispectral pansharpening. Here, we extend the capabilities of NNDiffuse to perform image fusion of high-res multispectral and low-res hyperspectral images (HRMSI+LRHSI fusion). Unlike learning-based frameworks which are computationally expensive and require extensive optimization and/or training time, NNDiffuse is fast, radiometrically accurate (introduces less spectral distortions compared to other state-of-the-art methods), and requires no training data. We introduce the utility of NNDiffuse in hyperspectral-panchromatic and hyperspectral-multispectral sharpening and look at workflows dealing with low-res HSI bands that fall outside the HRMSI spectral response functions. Sharpened image quality is assessed using image-wide metrics. Sharpening performance is also measured on the fused images in terms of their utility in pixel classification and ACE target-background separability. NNDiffuse and DL- and non DL-based methods are presented using hyperspectral imagery from SHARE2012, AVIRIS-NextGen, Hyperspec-VNIR-C, and ROSIS sensors.
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