DocumentCode :
2779091
Title :
ROEWA based detector for SAR automatic target recognition
Author :
Ranjani, Jennifer J. ; Iyalkanimozhi, E. ; Priyadharshini, D. ; Thiruvengadam, S.J. ; Babu, M.
Author_Institution :
Dept. of Inf. Technol., Thiagarajar Coll. of Eng., Madurai
fYear :
2008
fDate :
18-20 Dec. 2008
Firstpage :
1
Lastpage :
5
Abstract :
Edge detection is a fundamental issue in automatic target detection using synthetic aperture radar (SAR) images. Edges are associated with intensity changes in the image and are efficient descriptors of the image structure. Due to the presence of speckle, edge detection in SAR images is extremely difficult. Several detectors have been developed for the detection of isolated step edges in speckled images like MRoA and RGoA edge detectors which use predefined thresholds. The modified RGoA detector defines an automatic threshold determining method. But all these edge detectors, apart from detecting the target edges, detect a number of false edges. In this paper we have proposed a new ROEWA based algorithm that automatically discriminates the object boundaries and the false edges. The principle of entropy is introduced in this classification process. Real SAR images are used to verify our method and the results are compared with the modified RGoA and entropy based MRGoA edge detectors. Experimental results show that the proposed method is robust and efficient.
Keywords :
edge detection; radar imaging; synthetic aperture radar; MRoA edge detectors; RGoA edge detectors; ROEWA based detector; SAR automatic target recognition; synthetic aperture radar images; Detectors; Entropy; Image edge detection; Object detection; Optical scattering; Radar detection; Reflectivity; Speckle; Synthetic aperture radar; Target recognition; Automatic Target Recognition; Edge Detection; SAR Image;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Communication and Networking, 2008. ICCCn 2008. International Conference on
Conference_Location :
St. Thomas, VI
Print_ISBN :
978-1-4244-3594-4
Electronic_ISBN :
978-1-4244-3595-1
Type :
conf
DOI :
10.1109/ICCCNET.2008.4787730
Filename :
4787730
Link To Document :
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