![]() Therefore, only a relatively small number of photons are collected within each narrowed band. ![]() This is because when applied to the same region of the electromagnetic spectrum as multispectral sensors, hyperspectral sensors capture bands with higher density. However, the spatial resolution of HSIs is rather low compared to multispectral images. They have been extensively employed in fields such as military object recognition, geological exploration, and target detection. Unlike other forms of images, HSIs can provide a wider range of spectral information, which can be used to distinguish the objects in the image scene. 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.Ī hyperspectral image (HSI) is a three-dimensional data cube that records a set of two-dimensional images (or bands), which represent the reflectance or radiance of a scene at various electromagnetic wavelengths. 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. 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. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. 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. 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.
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