Loss Functions Analysis of Performance Improvements in Single-Image Super-Resolution

The building rooftop delineation plays a critical role in urban planning and management. Current methods on improving the delineation accuracy of the building rooftops mostly focused on the data improvements and methodology improvement. However, as the current deep learning networks have already reached high accuracy, the data quality has then become the important topic. Although loss functions serve the purpose of quantifying reconstruction errors and directing the optimization process of the model in super-resolution (SR), limited research related to their impact on SR were implemented. In this study, we focused on improving the spatial resolution of the building datasets by investigating numerous loss functions, including the Mean Absolute Error (MAE) loss function, the Mean-Squared Error (MSE) loss function, the SmoothL1 loss function and the Charbonnier loss function. With our proposed Single-Image Super-Resolution (SISR) network, Residual Feature Aggregation- Pyramid Vision Transformer- Involution Network (RFA-PVTInvoNet) and other typical SISR networks, the loss functions were applied to further enhance the performance. By using Peak Signal-Noise Ratio (PSNR) and Similarity Structure Index Measurement (SSIM) as the evaluation metrics, the RFA-PVTInvoNet with Charbonnier loss function showed the highest performance compared with other approaches, with PSNR of 22.046 dB and SSIM of 0.502 on the WHU Building Dataset, which demonstrate the superior performance of the Charbonnier loss in SISR.