Road Extraction from High-Resolution Remote Sensing Images Based on Synthetical Characteristics

Abstract:

Road extraction is the recurring important application of high-resolution remote sensing images. In order to achieve the goal of road extraction, the various characteristics of geographic information of high-resolution remote sensing images as well as the application and models of road extraction are analyzed, then an effective way of extracting roads from high-resolution remote sensing images is found, and then the high-resolution remote sensing image road extraction algorithm based on texture characteristics assisted by other characteristic information is put forward. The specific process of road extraction in the algorithm is introduced, and the function of road extraction of urban high-resolution remote sensing image based on texture characteristics is also tested practically, the result shows that this method has a higher degree of accuracy in extracting roads from urban high-resolution remote sensing images.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 828-831
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.828
• Online since: July 2013
• Authors: Yong Sheng Chen, Zhi Jia Hong, Qun He, Hong Bin Ma
• Keywords: Mathematic Morphology, Road Extraction, Synthetical Characteristic

References

[1] Wen, G. J. and R. Wang. 2000. Automatic extraction of main roads in [J], Journal of software from aerial remote sensing images, 11 (7):957 ~ 964.

[2] Steger, C. 1998. An Unbiased Detector of Curvilinear Structures[J].IEEE Trans. On Pattern Analysis and Machine Intelligence. 20(2):113-125.

DOI: 10.1109/34.659930

[3] Wiedemann, C. and S. Hinz. 1999. Automatic Extraction and Evaluation of Road Networks from Satellite Imagery [A].International Archives of Photogrammetry and Remote Sensing[C]., 32(3-2W5):95-100.

[4] Haverkamp, D. 2002. Extracting straight road structure.in urban environments using IKONOS satellite imagery[J].Optical Engineering.41(9):2107-2110.

DOI: 10.1117/1.1496785

[5] Wang, D. and T. Jiang. 2004. Fuzzy operator theory of semi - automatic road extraction based on [J], Journal of Institute of Surveying and mapping, 21 (31): 187-189.

[6] Haralick, R.M. and K. Shanmygam, I.H. Dinstein. 1973. Textual Features for Image Classification. IEEETransactions on systems,man and cybernetics, vol.3, Issue. (6):610～621.

DOI: 10.1109/tsmc.1973.4309314

[7] Yuan F, Z. Meng and G. Yu. 2004. The improved [J] clustering algorithm based on K-means, computer engineering and applications.

[8] Gao J. and Z. Xie. 2009. Image understanding theory and method [M], Beijing: Science Press.