Road Extraction From a High Spatial Resolution Remote Sensing Image Based on Richer Convolutional Features

The extraction and vectorization of roads from high spatial resolution remote sensing (HSRRS) images are of great significance to city planning and development. However, significant as they are, it is usually an arduous task to put them into practice because the HSRRS images are often filled with complex ground information. Furthermore, extracted roads may suffer from netsplit or brokenness. This paper thus proposes Richer convolutional features (RCFs)-based road extraction (Road-RCF) as a method which targets these issues. A modified roads sample set and RCF network are applied to generate road probabilities in order to extract initial road information. After the road centerlines extraction by the refinement algorithm, vectorized roads are ultimately extracted. The compared experiment results show that the Road-RCF method produce better road extraction results than the other four state-of-the-art methods, in both quantitative road extraction accuracy metrics and the qualitative visual evaluation. The benefits of this model are threefold. First, the image-to-image network structure of side-output realizes multi-scale and multi-level road feature fusion in order to make a full use of the information from a low level to a high level. Second, according to the deep supervision of the side-output, it guides the learning of the correct road information. Third, after the detection of the road, the road centerlines are vectorized to facilitate the attribute information management and electronic map production. In a word, the proposed Road-RCF method is both practical and meaningful toward updating the geo-information system database.