Road Recognition From Remote Sensing Imagery Using Incremental Learning
Roads, as important artificial objects, are the main body of modern traffic system, providing many conveniences for human civilization. With the development of Intelligent Transportation Systems (ITS), the road structure is changing frequently. Road recognition is to identify the road type from remote sensing imagery, and road types depend largely on the characteristics of roads. Thus, how to extract road features and further making road classification efficient have become a popular and challenging research topic. In this paper, we propose a road recognition method for remote sensing imagery using incremental learning. In principle, our method includes the following steps: 1) the non-road remote sensing imagery is first filtered by using support vector machine; 2) the road network is obtained from the road remote sensing imagery by computing multiple saliency features; 3) the road features are extracted from road network and background environment; and 4) the roads are recognized as three road types according to the classification results of incremental learning algorithm. The experimental results show that our method has higher road recognition rate as well as less recognition time than the other popular algorithms.