Hyperspectral remote sensing can get spatially and spectrally continuous data simultaneously. However, the imaging equipment is usually expensive and complex, along with the low spatial resolution. In recent years, reconstruction of hyperspectral image by deep learning from the widely used low-cost, high spatial resolution RGB camera, has attracted extensive attention in many fields. However, most research is limited to three bands in the range of 400–700 nm, which greatly restrains its application in remote sensing. In this study, a more suitable for remote sensing multispectral to hyperspectral network (M2H-Net) is proposed, which can take many bands as input and output hyperspectral images with any number of bands within a wider spectral range (380–2500 nm). Its characteristics include adding residual connection on U-Net to reduce vanishing gradients; adding convolution combinations with different kernel sizes (1 × 1 and 3 × 3) to balance the spectral and spatial relationships. It is applied on images from different platforms (UAVs and Satellites), different imaging modes (frame and pushbroom) and different spectral response functions (narrow and wide bandwidth), and the results show that: 1) it has a very high accuracy of hyperspectral image reconstruction. The mean relative absolute error (MRAE) and root mean squared error (RMSE) are between 0.039 and 0.074 and 0.010–0.016, respectively, which are 69.2% and 41.2% lower than those of U-Net; 2) it has high efficiency with fast convergence (about 40 epochs) and stable performance. Compared with many algorithms won in the new trends in image restoration and enhancement (NTIRE) competition, M2H-Net ranked 7th in accuracy, but took less time (0.44 s); 3) it has strong generalization ability. Using the pre-trained M2H-Nets to reconstruct Cubert S185 and GF-5 hyperspectral images in different locations, different times and complex scenes, high accuracy (MRAE = 0.072, RMSE = 0.011) can still be obtained. This method is more suitable for remote sensing to meet the needs of multiple bands, spectrum width and complex scenes, thus provides the possibility to generate the global coverage hyperspectral imagery by using the massive in-orbit or historical archived multispectral images, which will not only greatly save the R&D and investment on hyperspectral imaging equipment, but also conduct data collection with higher efficiency and lower complexity. Due to the ability to reconstruct hyperspectral images in specified bands on demand, M2H-Net is also of great value in hyperspectral image processing, such as data compression, storage and transmission, etc.

高光谱遥感可以同时获取空间和光谱连续数据。然而，成像设备通常昂贵且复杂，并且空间分辨率低。近年来，通过从广泛使用的低成本，高空间分辨率的RGB相机中进行深度学习来重建高光谱图像，已在许多领域引起了广泛关注。但是，大多数研究仅限于400-700 nm范围内的三个波段，这极大地限制了其在遥感中的应用。在这项研究中，提出了一种更适合于遥感多光谱到高光谱网络（M2H-Net）的方法，它可以将许多波段作为输入和输出的高光谱图像，并在更宽的光谱范围（380–2500 nm）中具有任意数量的波段。它的特征包括在U-Net上添加剩余连接以减少消失梯度；添加具有不同内核大小（1×1和3×3）的卷积组合以平衡频谱和空间关系。它应用于来自不同平台（UAV和卫星），不同成像模式（帧和推扫帚）和不同光谱响应函数（窄带宽和宽带宽）的图像，结果表明：1）它具有很高的高光谱精度影像重建。平均相对绝对误差（MRAE）和均方根误差（RMSE）分别在0.039和0.074和0.010-0.016之间，比U-Net分别低69.2％和41.2％；2）效率高，收敛速度快（约40个纪元），性能稳定。与在图像还原和增强（NTIRE）竞争的新趋势中获胜的许多算法相比，M2H-Net的准确性排名第七，但花费的时间更少（0.44 s）；3）具有很强的泛化能力。使用预训练的M2H-Nets在不同位置，不同时间和复杂场景下重建Cubert S185和GF-5高光谱图像，仍可以获得高精度（MRAE = 0.072，RMSE = 0.011）。该方法更适合于遥感，以满足多波段，频谱宽度和复杂场景的需求，从而提供了通过使用大规模在轨或历史存档的多光谱图像来生成全球覆盖的高光谱图像的可能性，这不仅会大大节省了高光谱成像设备的研发和投资，而且还能以更高的效率和更低的复杂度进行数据收集。由于能够按需重建指定频段中的高光谱图像，因此M2H-Net在高光谱图像处理（例如数据压缩，存储和传输等）中也具有重要价值。