Title: Hybrid Deep Learning for Hyperspectral Single Image Super-Resolution Show Chinese title

Title: 高光谱单图像超分辨率的混合深度学习

Abstract: Hyperspectral single image super-resolution (SISR) is a challenging task due to the difficulty of restoring fine spatial details while preserving spectral fidelity across a wide range of wavelengths, which limits the performance of conventional deep learning models. To address this challenge, we introduce Spectral-Spatial Unmixing Fusion (SSUF), a novel module that can be seamlessly integrated into standard 2D convolutional architectures to enhance both spatial resolution and spectral integrity. The SSUF combines spectral unmixing with spectral--spatial feature extraction and guides a ResNet-based convolutional neural network for improved reconstruction. In addition, we propose a custom Spatial-Spectral Gradient Loss function that integrates mean squared error with spatial and spectral gradient components, encouraging accurate reconstruction of both spatial and spectral features. Experiments on three public remote sensing hyperspectral datasets demonstrate that the proposed hybrid deep learning model achieves competitive performance while reducing model complexity. Show Chinese abstract

Abstract: 高光谱单图像超分辨率（SISR）是一项具有挑战性的任务，因为恢复精细的空间细节同时在宽波长范围内保持光谱保真度存在困难，这限制了传统深度学习模型的性能。 为了解决这一挑战，我们引入了光谱-空间解混融合（SSUF），这是一种新型模块，可以无缝集成到标准的2D卷积架构中，以提高空间分辨率和光谱完整性。 SSUF结合了光谱解混与光谱-空间特征提取，并指导基于ResNet的卷积神经网络以实现更好的重建。 此外，我们提出了一种自定义的光谱-空间梯度损失函数，该函数结合了均方误差与空间和光谱梯度成分，鼓励对空间和光谱特征进行准确重建。 在三个公开的遥感高光谱数据集上的实验表明，所提出的混合深度学习模型在减少模型复杂度的同时实现了具有竞争力的性能。