DocumentCode
2144928
Title
An improved CFAR model for ship detection in SAR imagery
Author
Huang, Weigen ; Chen, Peng ; Yang, Jingsong ; Fu, Bin ; Xiao, Qingmei ; Yao, Lu ; Zhou, Changbao
Author_Institution
Lab. of Ocean Dynamic Processes & Satellite Oceanogr., Second Inst. of Oceanogr., Hangzhou
Volume
7
fYear
2004
fDate
20-24 Sept. 2004
Firstpage
4719
Abstract
This paper presents an improved constant false alarm rate (CFAR) model for ship detection in synthetic aperture radar (SAR) imagery. The model includes the probabilistic neural networks, CFAR technique, golden section method and area growth method. It is compared with other ship detection methods. The results show that the improved CFAR model performs well
Keywords
oceanographic techniques; radar detection; radar imaging; remote sensing by radar; ships; synthetic aperture radar; CFAR model; SAR imagery; constant false alarm rate; ship detection; synthetic aperture radar imagery; Backscatter; Equations; Gaussian processes; Marine vehicles; Neural networks; Oceans; Radar detection; Sea surface; Shape; Synthetic aperture radar;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
Conference_Location
Anchorage, AK
Print_ISBN
0-7803-8742-2
Type
conf
DOI
10.1109/IGARSS.2004.1370212
Filename
1370212
Link To Document