Multi-source remote sensing imagery collaboration method based on super-resolution reconstruction
Guang Li, Wenting Han, Jiaqi Wei, Mingsheng Shang, Diwen Xiong, Xuedong Zhai, Yuxin Dong, Liyuan Zhang · International Journal of Remote Sensing · 2024
Suitable remote sensing images are crucial for the accurate monitoring of land surface information. Multi-source remote sensing image collaboration is a viable method for obtaining suitable images. Image super-resolution (SR) reconstruction can transcend the mutual limitations of image monitoring range and spatial resolution and provide a solution for multi-source remote sensing image collaboration. However, image SR reconstruction experiences the challenges of low applicability of the modelling dataset, network model, and reconstruction methods. To solve these problems, in this study, two datasets were created: one containing only Gaofen-2 (GF-2) images and the other containing both GF-2 and Sentinel-2 images. Different SR models were established by combining the lightweight-enhanced SR convolutional neural network, enhanced SR generative adversarial network, and dual regression network (DRN). The applicability of the identified SR model was evaluated by applying it to the reconstruction of Sentinel-2 images from different spatiotemporal images. The results indicated that the model formed using the dataset containing both the GF-2 and Sentinel-2 images was highly accurate, with the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values of 23.2082 dB and 0.6408, respectively. The edges of the plots were refined, and the deformations of the plots, roads, and channels were restored. The SR model formed by the DRN was highly accurate for both single- and multi-source datasets, with PSNR and SSIM values of 24.1548 dB and 0.6912, respectively. Therefore, a method for forming an SR model using a multi-source image dataset combined with the DRN was proposed for multi-source remote sensing collaboration. The method proposed in this study has good applicability to different spatiotemporal images and can provide a reference for subsequent multi-source remote sensing research.