DocumentCode :
2168023
Title :
An Adaptive Corner Detection Algorithm for Remote Sensing Image Based on Curvature Threshold
Author :
Deng Xiaolian ; Huang Yuehua ; Feng Shengqin ; Wang Changyao
Author_Institution :
Key Lab. of Geol. Hazards on Three Gorges Reservoir Area, China Three Gorges Univ., Yichang, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
4
Abstract :
An adaptive corner detection algorithm for remote sensing image was discussed in this paper. The main proposal of this paper was to detect corner of remote sensing image automatically and intelligently. This paper proposed a novel corner detection algorithm, which confirmed the direction of corner by analyzing eight neighborhood direction gray gradients, then adopted the neighborhood gray gradient tracking method and two thresholds of gray gradient was adopted to detect the correct corner. By this corner detection method, corner of remote sensing image could be extracted correctly. Meanwhile, the threshold of discriminant function could be determined adaptively by calculating probability density curvature extremum of gray gradient instead of traditional empirical threshold. The result of the experiment demonstrated that, the algorithm could detect valuable ground control point, it had more detection accuracy and efficiency, and it had more adaptability and applicable prospect. The result of ground control point detection was more objective and dependable.
Keywords :
gradient methods; image matching; adaptive corner detection algorithm; curvature threshold; neighborhood gray gradient tracking method; probability density curvature extremum; remote sensing image; Algorithm design and analysis; Automatic control; Detection algorithms; Geology; Hazards; Image matching; Image processing; Laboratories; Remote sensing; Reservoirs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5304563
Filename :
5304563
Link To Document :
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