1.College of Information Engineering, Xiangtan University, Xiangtan 411105, China 2.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
The extraction method based on the edge features is widely used in the road recognition of remote sensing image. However, the traditional methods are not good at eliminating noise, and tend to cause the misjudgment and leak-judgment of the edge. Therefore, based on the idea of the canny edge detection algorithm, the authors firstly adopt a smoothing and self-adapting Gaussian filter to reduce the noise of remote sensing image, reduce the noise interference and reserve the edge and details. Then, in the edge judgment of the dual threshold, the authors select the high and low thresholds on the basis of local characteristics within the object scale of the pixel point and enhance the exact judgment performance of the edge. The experiment results show that the new method can effectively improve the accuracy and positioning accuracy of the edge detection, obviously reduce the misjudgment of road edge extraction and remarkably increase integrity and consecutiveness, with high automation.
Hu H L, Wu B, Huang S M . Urban road extraction of high resolution remotely sensed imagery with Gabor texture and geometrical features[J]. Journal of Geomatics Science and Technology, 2015,32(4):395-400.
Su T F, Li H Y, Qu Z Y . A study of road segmentation from the high resolution remote sensing image[J]. Remote Sensing for Land and Resources, 2015,27(3):1-6.doi: 10.6046/gtzyyg.2015.03.01.
Chen G, Chen L C, He X F , et al. A semiautomatic extraction method for main-road in high resolution imagery[J]. Remote Sensing Information, 2017,32(3):109-114.
Tan Y, Huang H X, Xu J M , et al. Road edge detection from remote sensing image based on improved Sobel operator[J]. Remote Sensing for Land and Resources, 2016,28(3):7-11.doi: 10.6046/gtzyyg.2016.03.02.
[6]
Canny J . A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986,8(6):679-698.
doi: 10.1109/TPAMI.1986.4767851
[7]
Saha P K, Udupa J K, Odhner D . Scale-based fuzzy connected image segmentation:Theory,algorithms, and validation[J]. Computer Vision and Image Understanding, 2000,77(2):145-174.
doi: 10.1006/cviu.1999.0813
[8]
Chen K . Adaptive smoothing via contextual and local discontinuities.[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(10):1552-1567.
doi: 10.1109/TPAMI.2005.190
pmid: 16237991
Qian X L, Guo L, Yu B . Adaptive Gaussian filter based on object scale[J]. Computer Engineering and Applications, 2010,46(12):14-16,20.
[10]
韦玉春, 汤国安, 汪闽 . 遥感数字图像处理教程[M]. 北京: 科学出版社, 2015.
Wei Y C, Tang G A, Wang M. Remote Sensing Digital Image Processing Tutorial[M]. Beijing: China Science Press, 2015.
[11]
Gonzalez R C, Woods R E, Eddins S L, 等. 数字图像处理(MATLAB版)[M]. 2版.北京: 电子工业出版社, 2013.
Gonzalez R C, Woods R E. Digital Image Processing Using MATLAB[M].2nd ed. Beijing: Electronics Industry Press, 2013.
[12]
Abdou I E, Pratt W K . Quantitative design and evaluation of enhancement/thresholding edge detectors[J]. Proceedings of the IEEE, 1979,67(5):753-763.
doi: 10.1109/PROC.1979.11325
[13]
秦彦光 . 高分辨率遥感图像道路网及车辆信息提取[D]. 长春:吉林大学, 2014.
Qin Y G . Study on Road Network and Automobile Information Extraction Based on High Resolution Remote Sensing Image[D]. Changchun:Jilin University, 2014.