Advanced road extraction using CNN-based U-Net model and satellite imagery

Abstract

Road extraction from high-resolution remote sensing imagery (HRRSI) is a challenging task due to low spectral variation and the presence of complex background elements, such as the shadows of buildings and trees. Many techniques have struggled to maintain proper edges and boundaries while retaining the geometric features necessary for accurate non-linear road extraction. In this paper, we address these issues by proposing a deep learning approach that utilizes a fine-tuned U-Net model with a modified feature space in the basic U-Net architecture for semantic segmentation. We also incorporate the BRISQUE preprocessing technique to improve the performance. We experimented with a subset of 200 high-quality images from the Massachusetts roads dataset, prioritized based on their rank. Due to computer memory constraints, the images were resized to 256 by 256 pixels. The proposed method produced the accuracy of 95.45%.