论文标题
光谱响应函数引导了深度优化驱动的网络，用于光谱超分辨率
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution
论文作者
论文摘要
高光谱图像对于许多研究工作至关重要。光谱超分辨率（SSR）是一种用于从HR多光谱图像获得高空间分辨率（HR）高光谱图像的方法。传统的SSR方法包括模型驱动算法和深度学习。通过展开一种变分方法，本文提出了一个具有深度空间光谱先验的优化驱动的卷积神经网络（CNN），从而产生了物理上可解释的网络。与完全数据驱动的CNN不同，辅助光谱响应函数（SRF）用于指导CNNs与光谱相关性分组。此外，使用通道注意模块（CAM）和重新启动角度映射器损耗函数，以实现有效的重建模型。最后，对包括自然和遥感图像在内的两种类型的数据集进行了实验，证明了该方法的光谱增强效果。遥感数据集上的分类结果还验证了通过建议的方法增强的信息的有效性。
Hyperspectral images are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high spatial resolution (HR) hyperspectral images from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this paper proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior, resulting in physically interpretable networks. Unlike the fully data-driven CNN, auxiliary spectral response function (SRF) is utilized to guide CNNs to group the bands with spectral relevance. In addition, the channel attention module (CAM) and reformulated spectral angle mapper loss function are applied to achieve an effective reconstruction model. Finally, experiments on two types of datasets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method. And the classification results on the remote sensing dataset also verified the validity of the information enhanced by the proposed method.
