DocumentCode
2209588
Title
Mathematical Morphology Edge Detection Algorithm of Remote Sensing Image with High Resolution
Author
Liu Sheng ; Wang Xiaoyu ; Qiu Xinfa ; He Yongjian
Author_Institution
Coll. of Comput. & Software, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
1323
Lastpage
1326
Abstract
Based on characteristics of remote sensing images, with high spatial resolutions and mathematical morphology (MM) methods, MM edge detection algorithm were developed to process remote sensing images using multi-scale omni-directional structure elements. In this algorithm, the peak signal-to-noise ratio (PSNR) technique was created to acquire the self-adaptive scale weights and replace the method with fixed mean. Results suggest that: the purposed algorithms, compared with the traditional Canny edge detection operator and MM edge detection algorithm with simplex scale figure structure elements, can successfully resolve the contradictions between noise suppression and extraction of fine edge excellently, and anti-noise performance is strong. The image edge information can be used in extraction geometrical and texture features.
Keywords
edge detection; feature extraction; geophysical image processing; image resolution; image texture; mathematical morphology; noise; remote sensing; anti-noise performance; fine edge extraction; geometrical feature extraction; high spatial resolutions; mathematical morphology edge detection algorithm; multiscale omni-directional structure elements; noise suppression; peak signal-to-noise ratio technique; remote sensing image; self-adaptive scale weights; texture feature extraction; Data mining; Feature extraction; Image edge detection; Image resolution; Information science; Morphology; PSNR; Remote sensing; Software algorithms; Spatial resolution;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.730
Filename
5454598
Link To Document