Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/100711
| Title: | Use of colour transformation and the geodesic method for road centreline extraction from VHR satellite images | Authors: | Miao, Z Gao, L He, Y Wu, L Shi, W Samat, A Liu, S Li, J |
Issue Date: | 2019 | Source: | International journal of remote sensing, 2019, v. 40, no. 10, p. 4043-4058 | Abstract: | Seed point-based road extraction methods are vital for extracting road networks from satellite images. Despite its effectiveness, roads in very high-resolution (VHR) satellite images are complicated, such as road occlusion and material change. To tackle this issue, this paper proposes to use the colour space transformation and geodesic method. First, the test image is converted from Red-Green-Blue colour space to Hue-Saturation-Value colour space to reduce the material change influence. The geodesic method is subsequently applied to extract initial road segments that link road seed points provided by users. At last, the initial result is adjusted by a kernel density estimation method to produce centred roads. The presented method is quantitatively evaluated on three test images. Experiments show that the proposed method yields a substantial improvement over cutting-edge technologies. The findings in this study shine new light on a practical solution for road extraction from satellite images. | Publisher: | Taylor & Francis | Journal: | International journal of remote sensing | ISSN: | 0143-1161 | EISSN: | 1366-5901 | DOI: | 10.1080/01431161.2018.1558374 | Rights: | © 2018 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 02 Jan 2019 (published online), available at: http://www.tandfonline.com/10.1080/01431161.2018.1558374. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Gao_Use_Colour_Transformation.pdf | Pre-Published version | 3.73 MB | Adobe PDF | View/Open |
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