Graph-Based Image Segmentation for Road Extraction from Post-Disaster Aerial Footage

This research effort proposes a novel method for identifying and extracting roads from aerial images taken after a disaster using graph-based image segmentation. The dataset that is used consists of images taken by an Unmanned Aerial Vehicle (UAV) at the University of West Florida (UWF) after hurricane Sally. Ground truth masks were created for these images, which divide the image pixels into three categories: road, non-road, and uncertain. A specific pre-processing step was implemented, which used Catmull-Rom cubic interpolation to resize the image. Moreover, the Gaussian filter used in Efficient Graph-Based Image Segmentation is replaced with a median filter, and the color space is converted from RGB to HSV. The Efficient Graph-Based Image Segmentation is further modified by (i) changing the Moore pixel neighborhood to the Von Neumann pixel neighborhood, (ii) introducing a new adaptive isoperimetric quotient threshold function, (iii) changing the distance function used to create the graph edges, and (iv) changing the sorting algorithm so that the algorithm can run more effectively. Finally, a simple function to automatically compute the k (scale) parameter is added. A new post-processing heuristic is proposed for road extraction, and the Intersection over Union evaluation metric is used to quantify the road extraction performance. The proposed method maintains high performance on all of the images in the dataset and achieves an Intersection over Union (IoU) score, which is significantly higher than the score of a similar road extraction technique using K-means clustering.

Automated summary

This research proposes a novel method for identifying and extracting roads from aerial images taken after a disaster. The method uses graph-based image segmentation and includes modifications and improvements to the Efficient Graph-Based Image Segmentation. The proposed method achieves high performance in road extraction and outperforms a similar technique using K-means clustering.