Abstract
Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.
Highlights
With the continuous development of Internet of things (IoT) big data, mobile Internet, grid computing, cloud computing, and other new technologies, system integration becomes more complex
We propose a multistage and multifeature method based on tensor voting, which includes template matching, geometric feature of road and tensor voting to extract roads, and road intersections in highresolution remotely sensed imagery
A multistage and multifeature method is proposed to extract roads and road intersections from high-resolution remote sensing images based on template matching and tensor voting
Summary
With the continuous development of Internet of things (IoT) big data, mobile Internet, grid computing, cloud computing, and other new technologies, system integration becomes more complex. Remote sensing big data is a revolution of traditional data processing and information extraction methods [1]. The great success of deep learning in the field of computer vision [32,33,34] provides an important opportunity for big data to extract information intelligence from remote sensing imagery. Due to the variety of road forms and the complexity of surrounding environment in reality, most of the existing methods extract roads from specific remote sensing images and road information of specific areas. We propose a multistage and multifeature method based on tensor voting, which includes template matching, geometric feature of road and tensor voting to extract roads, and road intersections in highresolution remotely sensed imagery.
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