Rajaei Arash, Abiri Ebrahim, Helfroush Mohammad Sadegh
Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.
Sci Rep. 2024 Nov 30;14(1):29820. doi: 10.1038/s41598-024-81031-8.
Hyperspectral-multispectral image fusion (HSI-MSI Fusion) for enhancing resolution of hyperspectral images is a hot topic in remote sensing. An important category of approaches for HSI-MSI Fusion is based on deep learning. The main challenges in deep learning based fusion methods include the lack of training data, poor generalization to various datasets, and high computational costs. This paper suggests a new approach to tackle these difficulties by introducing an innovative technique for HSI-MSI fusion. The proposed method involves training a tiny deep neural network that can reconstruct high-resolution hyperspectral images through spectral super-resolution of high-resolution multispectral images. This method does not require high resolution training data and they are artificially generated based on the spatial degradation model of the input observation images. Therefore, the problems of data scarcity and poor generalization are addressed, and also the computational burden is significantly reduced. After conducting thorough experiments, it was found that the proposed method provides promising results. The source code of this method is available at https://github.com/rajaei-arash/SSSR-HSI-MSI-Fusion .
用于提高高光谱图像分辨率的高光谱 - 多光谱图像融合（HSI - MSI融合）是遥感领域的一个热门话题。HSI - MSI融合的一类重要方法基于深度学习。基于深度学习的融合方法中的主要挑战包括训练数据不足、对各种数据集的泛化能力差以及计算成本高。本文提出了一种新方法来解决这些难题，即引入一种用于HSI - MSI融合的创新技术。所提出的方法涉及训练一个小型深度神经网络，该网络可以通过高分辨率多光谱图像的光谱超分辨率来重建高分辨率高光谱图像。此方法不需要高分辨率训练数据，而是基于输入观测图像的空间退化模型人工生成训练数据。因此，解决了数据稀缺和泛化能力差的问题，并且计算负担也显著降低。经过全面实验发现，所提出的方法取得了有前景的结果。该方法的源代码可在https://github.com/rajaei - arash/SSSR - HSI - MSI - Fusion获取。
