论文标题
高光谱图像超分辨率的空间光谱残差网络
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution
论文作者
论文摘要
基于深度学习的高光谱图像超分辨率（SR）方法最近取得了巨大的成功。但是，大多数现有模型无法同时探索频段之间的空间信息和光谱信息，从而获得相对较低的性能。为了解决这个问题，在本文中，我们提出了一个新型的光谱空间残留网络，用于高光谱图像超分辨率（SSRNET）。我们的方法可以通过使用3D卷积而不是2D卷积来有效地探索空间光谱信息，这使网络能够更好地提取潜在信息。此外，我们设计了光谱空间残差模块（SSRM），以通过局部特征融合从单位中的所有分层特征中自适应地学习更有效的特征，从而显着提高了算法的性能。在每个单元中，我们都采用空间和时间可分离的3D卷积来提取空间和光谱信息，这不仅减少了无法负担的内存使用和高计算成本，而且还使网络更易于训练。在三个基准数据集上进行了广泛的评估和比较表明，与现有的最新方法相比，所提出的方法可以达到卓越的性能。
Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, most existing models can not effectively explore spatial information and spectral information between bands simultaneously, obtaining relatively low performance. To address this issue, in this paper, we propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet). Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. Furthermore, we design a spectral-spatial residual module (SSRM) to adaptively learn more effective features from all the hierarchical features in units through local feature fusion, significantly improving the performance of the algorithm. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.
