融合光谱解混与梯度损失，提升高光谱图像超分辨率重建质量
Hybrid Deep Learning for Hyperspectral Single Image Super-Resolution
- 引入光谱-空间解混融合模块，增强特征提取能力
- 在三个公开数据集上实现优异重建效果，模型更轻量
- 适合遥感图像处理、高光谱分析等领域的研究人员
高光谱单图像超分辨率（SISR）因需在宽波段范围内同时恢复精细空间细节并保持光谱保真度，极具挑战性，传统深度学习模型性能受限。为此，我们提出光谱-空间解混融合（SSUF）模块，可无缝嵌入标准2D卷积架构中，以提升空间分辨率和光谱完整性。SSUF结合光谱解混与光谱-空间特征提取，引导基于ResNet的卷积神经网络进行优化重建。此外，设计了一种自定义的光谱-空间梯度损失函数，融合均方误差与空间及光谱梯度成分，促进空间与光谱特征的准确重构。在三个公开遥感高光谱数据集上的实验表明，所提混合深度学习模型性能优越且模型复杂度更低。
原文摘要 · Abstract (English)
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.
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