Road detection using Deep Neural Network in high spatial resolution images

Object detection is one of the mandatory steps in transferring imagery data into land cover information. Deep networks in machine learning have shown capabilities in automatic object detection and generated promising results. The patch-based Deep Neural Network (DNN) is one of the architectures that is designed for a pixel based object detection in aerial images. The network was designed for the images with 1.2 m spatial resolution, thus, it was unable to generate promising results for a large orthophoto aerial data set obtained over Fredericton city with 0.15 m spatial resolution. In this paper, the patched-based deep neural network is further improved for detecting roads in Fredericton data set. The network is redesigned based on our data and then, trained and applied to the data set. Results are evaluated qualitatively and quantitatively using Precision and Recall (P-R) method, and are compared with the result of Support Vector Machines (SVMs) method. Results of the adapted Deep Neural Network method show 0.89 accuracy, more than SVMs (0.78), making it applicable for road detection in large-scale data sets.