Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.

中文翻译：

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基于3D Attention-SRGAN网络的高光谱遥感图像超分辨率

与多光谱遥感影像相比，高光谱遥感影像（HSI）具有更高的光谱分辨率，为更加合理和有效地分析和处理光谱数据提供了可能性。然而，由于传感器的物理限制，丰富的光谱信息通常以低空间分辨率为代价，这给识别和分析HSI中的目标带来了困难。在超分辨率（SR）领域，许多方法一直侧重于空间信息的恢复，而忽略了光谱方面。为了更好地恢复HSI SR领域的光谱信息，本研究提出了一种新的超分辨率（SR）方法。首先，我们创新地使用了基于SRGAN（超分辨率生成对抗网络）结构的三维（3D）卷积，不仅可以利用空间特征，而且可以在SR过程中保留频谱特性。此外，我们使用注意力机制来处理3D卷积层中的乘法特征，并通过改善生成器损失函数的内容来增强模型的输出。实验结果表明，3DASRGAN（基于3D注意力的超分辨率生成对抗网络）在视觉上都比比较方法更好，这证明3DASRGAN模型可以高效地重建高分辨率HSI。我们使用注意力机制来处理3D卷积层中的乘法特征，并通过改进生成器损失函数的内容来增强模型的输出。实验结果表明，3DASRGAN（基于3D注意力的超分辨率生成对抗网络）在视觉上都比比较方法更好，这证明3DASRGAN模型可以高效地重建高分辨率HSI。我们使用注意力机制来处理3D卷积层中的乘法特征，并通过改进生成器损失函数的内容来增强模型的输出。实验结果表明，3DASRGAN（基于3D注意力的超分辨率生成对抗网络）在视觉上都比比较方法更好，这证明3DASRGAN模型可以高效地重建高分辨率HSI。