The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 31–37, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-31-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 31–37, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-31-2022
 
30 May 2022
30 May 2022

IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION

H. He1, K. Gao1, W. Tan1, L. Wang1, S. N. Fatholahi1, N. Chen1,2, M. A. Chapman3, and J. Li1,4 H. He et al.
  • 1Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
  • 2College of Geological Engineering and Geomatics, Chang’an University, Xi’an, SX710054, China
  • 3Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
  • 4Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Keywords: Deep Learning, Super-resolution, Building Footprint Extraction, Impact, HRNet v2, the Massachusetts Building Dataset

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.