Multiple Saliency Features Based Automatic Road Extraction from High-Resolution Multispectral Satellite Images

2018 ◽  
Vol 27 (1) ◽  
pp. 133-139 ◽  
Author(s):  
Jing Zhang ◽  
Lu Chen ◽  
Li Zhuo ◽  
Wenhao Geng ◽  
Chao Wang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Road Extraction ◽  
Multispectral Satellite Images

scholarly journals AUTOMATIC CENTERLINE EXTRACTION OF COVERD ROADS BY SURROUNDING OBJECTS FROM HIGH RESOLUTION SATELLITE IMAGES

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 111-116 ◽  
Author(s):  
H. Kamangir ◽  
M. Momeni ◽  
M. Satari
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Ground Truth ◽  
Road Extraction ◽  
Line Extraction ◽  
Road Map ◽  
High Resolution Satellite Images ◽  
Three Stages ◽  
Classification Of Images

This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.

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