DocumentCode :
1997779
Title :
Research on Vehicle Detection in High Resolution Satellite Images
Author :
Lizhu Xie ; Liying Wei
Author_Institution :
Sch. of Traffic & Transp., Beijing Jiaotong Univ., Beijing, China
fYear :
2013
fDate :
3-4 Dec. 2013
Firstpage :
279
Lastpage :
283
Abstract :
With the improvement of satellite resolution and the object-oriented detection method in satellite images, traffic data can be more quickly and widely acquired in large area satellite images compared with the traditional data acquired method. With the technology of image enhancement, the paper improved the image quality first, and then utilized the multi-scale segmentation technology and supervised classification method to detect the vehicle from satellite images. In the process, three classification decision trees for vehicles in different situations have been summed up. At last, the paper has achieved the empirical research using the remote sensing images of typical regions in the urban road from Worldview-2 and the GeoEye-1. Based on the precision analysis of the experimental results, it shows that the average accuracy is more than 90%.
Keywords :
artificial satellites; decision trees; image classification; image enhancement; image resolution; image segmentation; object detection; remote sensing; road vehicles; traffic information systems; GeoEye-1; Worldview-2; classification decision trees; empirical analysis; high-resolution satellite images; image enhancement; image quality improvement; large-area satellite images; multiscale segmentation technology; object-oriented detection method; precision analysis; remote sensing images; supervised classification method; traffic data; urban road; vehicle detection; Accuracy; Brightness; Image resolution; Roads; Satellites; Vehicle detection; Vehicles; high resolution satellite images; multi-scale; object-oriented image analysis; vehicle detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems (GCIS), 2013 Fourth Global Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4799-2885-9
Type :
conf
DOI :
10.1109/GCIS.2013.51
Filename :
6805948
Link To Document :
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