Hyperspectral imagery has been widely used in military and civilian research fields such as crop yield estimation, mineral exploration, and military target detection. However, for the limited imaging equipment and the complex imaging environment of hyperspectral images, the spatial resolution of hyperspectral images is still relatively low, which limits the application of hyperspectral images. So, studying the data characteristics of hyperspectral images deeply and improving the spatial resolution of hyperspectral images is an important prerequisite for accurate interpretation and wide application of hyperspectral images. The purpose of this paper is to deal with super-resolution of the hyperspectral image quickly and accurately, and maintain the spectral characteristics of the hyperspectral image, makes the spectral separability of the substrate in the original image remains unchanged after super-resolution processing. This paper first learns the mapping relationship between the spectral difference of low-resolution hyperspectral image and the spectral difference of the corresponding high-resolution hyperspectral image based on multiple scale convolutional neural network, Thus, apply this mapping relationship to the input low-resolution hyperspectral image generally, getting the corresponding high resolution spectral difference. Constrained space by using the image of reconstructed spectral difference, this requires the low-resolution hyperspectral image generated by the reconstructed image is to be close to the input low-resolution hyperspectral image in space, so that the whole process becomes a closed circulation system where the low-resolution hyperspectral image generation of high-resolution hyperspectral images, then back to low-resolution hyperspectral images. This innovative design further enhances the super-resolution performance of the algorithm. The experimental results show that the hyperspectral image super-resolution method based on convolutional neural network improves the input image spatial information, and the super-resolution performance of the model is above 90%, which can maintain the spectral information well.

高光谱图像已广泛用于军事和民用研究领域，例如作物产量估计，矿物勘探和军事目标检测。但是，由于有限的成像设备和高光谱图像的复杂成像环境，高光谱图像的空间分辨率仍然较低，这限制了高光谱图像的应用。因此，深入研究高光谱图像的数据特征，提高高光谱图像的空间分辨率，是正确解释和广泛应用高光谱图像的重要前提。本文的目的是快速，准确地处理高光谱图像的超分辨率，并保持高光谱图像的光谱特性，使得基片在原始图像中的光谱可分离性经过超分辨率处理后保持不变。本文首先基于多尺度卷积神经网络学习了低分辨率高光谱图像的光谱差异与相应高分辨率高光谱图像的光谱差异之间的映射关系，从而将该映射关系应用于输入的低分辨率高光谱图像通常，获得相应的高分辨率光谱差异。通过使用重构光谱差的图像来约束空间，这要求重构图像生成的低分辨率高光谱图像要与空间中输入的低分辨率高光谱图像接近，从而使整个过程成为一个封闭的循环系统，在该系统中，低分辨率高光谱图像生成高分辨率高光谱图像，然后又回到低分辨率高光谱图像。这种创新的设计进一步增强了算法的超分辨率性能。实验结果表明，基于卷积神经网络的高光谱图像超分辨率方法改善了输入图像的空间信息，模型的超分辨率性能达到90％以上，可以很好地保持光谱信息。