نشریه سنجش از دور و GIS  ایران

نشریه سنجش از دور و GIS ایران

Refinement of Road Extraction Based on Deep Learning Methods in UAV Images

نوع مقاله : مقاله پژوهشی

نویسندگان
Dep of Geodesy and Surveying Engineering, Tafresh University, Tafresh, Iran
چکیده
Road extraction from remotely sensed images plays an important role in urban planning, navigation systems, traffic management, mapping, and monitoring. In the last decade, several advanced machine learning methods have been developed alongside data acquisition technologies. In this study, a new post-processing method is developed to enhance the results of convolutional neural networks (CNNs) by injecting spatial information. For this purpose, Xception, a powerful deep learning network, is implemented to extract roads in UAV images. Although it detects the road accurately, there are still false positive and negative pixels in the classification map. In this study, a post-processing step based on spatial information is proposed to decrease the FP and FN. Several morphological operators are considered in this step, and the road boundaries with their direction are also computed. The fusion of the obtained results based on Xception and the spatial information of the post-processing stage significantly improves the road extraction results. The proposed method was evaluated using the NITR and UDD datasets. The obtained results on NITR show that Xception achieved 93.81%, while UNet and SegNet achieved 85.97% and 77.11%, respectively. On the UDD dataset, XCeption achieved 91.94%, whereas UNet and SegNet achieved 83.19% and 75.67%, respectively. The proposed method can improve the Xception results by up to 96.08% and 94.47%, respectively.
کلیدواژه‌ها

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