M2H-Net: A Reconstruction Method For Hyperspectral Remotely Sensed Imagery

Abstract

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.