Improved Road Detection Algorithm Based on Fusion of Deep Convolutional Neural Networks and Random Forest Classifier on VHR Remotely-Sensed Images

Detailed maps and road maps have broad and vital applications in military, urban design, crisis management, and accidents. Today, on the one hand, with the development of remote sensing sciences, the use of aerial and satellite images has expanded, and on the other hand, new advances in the processing of these images have led to improved analysis results on remote sensing images. One of the most up-to-date branches of image processing is deep learning networks, which have wide applications in various fields of engineering sciences, including remote sensing sciences. In this study, to extract the roads, the deep learning method with the customized U-Net architecture was used on the dataset of Massachusetts, USA, which were published in free access. Many studies have been done on this data, which generally emphasizes the architecture and methods of post-processing. In this research, focusing on data, deep learning networks have been used in two different scenarios. Scenario 1 of RGB images and Scenario 2, an additional channel with a random forest road/on-road classified image, is added to the RGB images and trained by the network. The results of the U-Net network training indicate very high accuracy of the F1-score average of 0.92 for Scenario 2. The method in this article can be used as a reliable method in road detection.