Hyperspectral (HS) pansharpening aims at fusing a low-resolution HS (LRHS) image with a panchromatic image to obtain a full-resolution HS image. Most of the existing HS pansharpening approaches are usually based on traditional multispectral pansharpening techniques, which are not especially tailored for two inherent challenges of the HS pansharpening, i.e., much wider spectral range gap between the two kinds of images and having to recover details in many continuous spectral bands simultaneously. In this article, we develop new spectral-fidelity convolutional neural networks (called HSpeNets) for HS pansharpening to keep the fidelity of a pansharpened image to its true spectra as much as possible. Our methods particularly focus on the decomposability of HS details, accordingly synthesizing these details progressively, and meanwhile introduce a spectral-fidelity loss. We give theoretical justifications and provide detailed experimental results, showing the superiorities of the proposed HSpeNets with regard to other state-of-the-art pansharpening approaches.

中文翻译：

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用于高光谱全色锐化的光谱保真度卷积神经网络


高光谱（HS）全色锐化旨在将低分辨率HS（LRHS）图像与全色图像融合以获得全分辨率HS图像。大多数现有的 HS 全色锐化方法通常基于传统的多光谱全色锐化技术，这些技术并不是专门针对 HS 全色锐化的两个固有挑战而设计的，即两种图像之间的光谱范围差距要大得多，并且必须在许多图像中恢复细节。同时连续的光谱带。在本文中，我们开发了用于 HS 全色锐化的新光谱保真度卷积神经网络（称为 HSpeNets），以尽可能保持全色锐化图像与其真实光谱的保真度。我们的方法特别关注 HS 细节的可分解性，相应地逐步合成这些细节，同时引入频谱保真度损失。我们给出了理论依据并提供了详细的实验结果，展示了所提出的 HSpeNet 相对于其他最先进的全色锐化方法的优越性。