Enhancing Spatial Resolution of Building Datasets Using Transformer-Based Single-Image Super-Resolution

The spatial resolution of Earth Observation (EO) images plays a key role in building footprint extraction. For the spatial resolution enhancement, deep learning-based image super-resolution methods have been widely used due to their remarkable performance. Transformer-based networks are effective and has drawn much attention in computer vision but underutilized in remote sensing, especially for super-resolving building datasets. Therefore, in this paper, we developed a novel transformer-based Single-Image Super-Resolution (SISR) method, named Pyramid Vision Transformer-Residual Feature Aggregation Network (PVT_RFANet), to improve the spatial resolution of building datasets. Specifically, the PVT v2 network was embedded into our Momentum Spatial-Channel Attention Residual Feature Aggregation Network (MSCA-RFANet). Moreover we conducted a comparative study to compare our method with Bicubic interpolation (BI), Super-resolution Convolutional Neural Network (SRCNN), Deep Recursive Residual Network (DRRN), SRResNet, and MSCA-RFANet. Using Peak Signal-Noise Ratio (PSNR) and Similarity Structure Index Measurement (SSIM) as the evaluation metrics, our method showed highest performance with the PSNR of 22.01 dB and the SSIM of 0.50 on the WHU Building Dataset, which demonstrated the superior performance of the proposed method.