Semi-automatic road extraction from high resolution satellite images by template matching using Kullback–Leibler divergence as a similarity measure. (2nd October 2022)

• Record Type: Journal Article
• Authors: Lin, Xiangguo; Xie, Wenhan; Zhang, Libo; Sang, Huiyong; Shen, Jing; Cui, Shiyong
• Is Part Of: International journal of image and data fusion. Volume 13:Number 4(2022)
• Page Start: 316
• Page End: 336
• Publication Date: 2022-10-02
• Subjects: Road tracking -- template matching -- Kullback–Leibler divergence -- very high spatial resolution remote sensing images; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2022.2121767
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Semi-automatic extraction of roads is greatly needed to accelerate the acquisition and updating of road maps. However, road surfaces are frequently disturbed on very high spatial resolution (VHSR) remotely sensed satellite imagery, which bothers the road trackers using least-squares-based template matching. This paper presents a novel semi-automatic framework for road tracking from VHSR satellite imagery. First, a human operator inputs three seed points. Second, the computer automatically tracks the road by the template matching using Kullback–Leibler divergence as a similarity measure. At the same time, a human operator is retained in the tracking process to supervise the extracted results, to response to the program's prompts. Once the failure or error happens, the human operator will correct the results and restart the automatic tracking. The above procedure is repeated until a whole road is tracked. Four satellite images with different complexities are used to perform experiments. The results show that our proposed road trackers is capable of automatically, accurately and fast extracting the long and high-level roads from the VHSR satellite images.