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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-8742-2
DOI :
10.1109/IGARSS.2004.1370212
