Automatic road extraction from high resolution remote sensing image by means of topological derivative and mathematical morphology

2018 ◽  
Author(s):  
Hongyu Zhou ◽  
Xu Song ◽  
Ying Guo
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mathematical Morphology ◽  
Remote Sensing Image ◽  
Topological Derivative ◽  
Road Extraction

Rural road extraction from high resolution remote sensing image

2015 ◽  
pp. 77-84
Author(s):  
Zh. Sun ◽  
Q. Meng ◽  
Z. Miao ◽  
X. Gu ◽  
Y. Zhan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Road Extraction ◽  
Rural Road

scholarly journals RESEARCH ON BROKEN ROAD CONNECTION METHOD AFTER ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 387-395
Author(s):  
D. L. Fan ◽  
B. Wang ◽  
Z. L. Chen ◽  
L. Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Power Grids ◽  
Road Extraction ◽  
The Road ◽  
Isolated Points

Abstract. Aiming at the problem of disconnection after road classification of remote sensing image, this paper proposes an optimization method for broken road connection considering spatial connectivity. The method extracts the road skeleton based on the binarized image after road extraction, and uses the eight neighborhood detection algorithm to find the road breakpoints after road extraction of high-resolution remote sensing image, and removes the isolated points of the road edge according to mathematical morphology filtering. Secondly, use K-means clustering algorithm to search for road breakpoints, and eliminate invalid breakpoints; then, fit the breakpoints of each category through polynomial curves, and record the mathematics of each fitted curve expression; Finally, the coordinate sequences between each kind of breakpoint is calculated according to each fitted polynomial, and the corresponding pixel is filled with the width of the road to realize automatic detection and connection. In this paper, the images after road extraction based on the U-Net network is used to test the method. The results show that the proposed method can better connect the roads formed by road or building shadows. Especially, the single broken road , has a high integrity of the road shape after repairing. The method proposed in this paper has certain reference significance for the classification and repair of linear objects such as roads, power grids and tracks.

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