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《Remote Sensing Technology and Application》 2018-01
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A New Ship Target Detection Algorithm based on SVM in High Resolution SAR Images

Xiong Wei;Xu Yongli;Yao Libo;Cui Yaqi;Institute of Information Fusion,Naval Aeronautical and Astronautical University;  
The characteristics of ocean background and target in the high resolution synthetic aperture radar(SAR)images are analyzed.Aiming at the requirements of ship detection in high-resolution synthetic aperture radar(SAR)image,the detection accuracy,intelligence level,real-time and processing efficiency,we put forward a high resolution SAR images ship detection algorithm based on support vector machine.The algorithm designs a pre-training support vector machine(SVM)classifier and complete the screening of the ship target block area,then the algorithm of optimal entropy thresholds proposed by Kapur,Sahoo,Wong(KSW)will be used on the target area selected for fine detection of ship targets.In this paper,several commercial satellite data,such as TerraSAR-X,are used to verify the experiment.Comparing with the classical CFAR detection algorithm,Experimental results show that the algorithm can improve the false alarm caused by the speckle noise and ocean clutter background inhomogeneity.At the same time,the detection speed is also increased by 20%to 35%.
【Fund】: 国家自然科学基金项目“空间信息网络对海上目标连续观测基础理论与关键技术”(42511133N)
【CateGory Index】: TN957.52
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