Impact of Deep Learning-Based Super-Resolution on Building Footprint Extraction

Abstract

Abstract. Automated building footprints extraction from High Spatial Resolution (HSR) remote sensing images plays important roles in urban planning and management, and hazard and disease control. However, HSR images are not always available in practice. In these cases, super-resolution, especially deep learning (DL)-based methods, can provide higher spatial resolution images given lower resolution images. In a variety of remote sensing applications, DL based super-resolution methods are widely used. However, there are few studies focusing on the impact of DL-based super-resolution on building footprint extraction. As such, we present an exploration of this topic. Specifically, we first super-resolve the Massachusetts Building Dataset using bicubic interpolation, a pre-trained Super-Resolution CNN (SRCNN), a pre-trained Residual Channel Attention Network (RCAN), a pre-trained Residual Feature Aggregation Network (RFANet). Then, using the dataset under its original resolution, as well as the four different super-resolutions of the dataset, we employ the High-Resolution Network (HRNet) v2 to extract building footprints. Our experiments show that super-resolving either training or test datasets using the latest high-performance DL-based super-resolution method can improve the accuracy of building footprints extraction. Although SRCNN based building footprint extraction gives the highest Overall Accuracy, Intersection of Union and F1 score, we suggest using the latest super-resolution method to process images before building footprint extraction due to the fixed scale ratio of pre-trained SRCNN and low speed of convergence in training.