The capability analysis of ship classification by structure feature using SAR images

ZHANG Xi; ZHANG Jie; JI Yong-gang; MENG Jun-min

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ZHANG Xi, ZHANG Jie, JI Yong-gang, MENG Jun-min. The capability analysis of ship classification by structure feature using SAR images[J]. Haiyang Xuebao, 2010, 32(1): 146-152.

Citation:

ZHANG Xi, ZHANG Jie, JI Yong-gang, MENG Jun-min. The capability analysis of ship classification by structure feature using SAR images[J]. Haiyang Xuebao, 2010, 32(1): 146-152.

ZHANG Xi, ZHANG Jie, JI Yong-gang, MENG Jun-min. The capability analysis of ship classification by structure feature using SAR images[J]. Haiyang Xuebao, 2010, 32(1): 146-152.

Citation:

ZHANG Xi, ZHANG Jie, JI Yong-gang, MENG Jun-min. The capability analysis of ship classification by structure feature using SAR images[J]. Haiyang Xuebao, 2010, 32(1): 146-152.

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The capability analysis of ship classification by structure feature using SAR images

1.
Institute of Computational Science and Engineering, Ocean University of China, Qingdao 266071, China;First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
2.
First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

Received Date: 2009-06-14

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References

MARGARIT G, MALLORQUI J J, RIUS J M, et al. On the usage of GRECOSAR, an orbital polarimetric SAR simulator of complex targets for vessel classification studies [J]. IEEE Trans Geosci Remote Sensing, 2006, 4412: 3517—3526.

MARGARIT G, FABREGAS X, MALLORQUI J J. Study of the vessel speed and sea swell effects on simulated polarimetric high resolution SAR images // STEIN T I. Proceedings of IGARSS2004. US IEEE 2004 International Geoscience and Remote Sensing Symposium. 2004: 603—606.

BACHMANN C M, MUSMAN S A, SCHULTZ A. Lateral inhibition neural networks for classification of simulated radar imagery // IEEE International Conference on Neural Networks. Baltimore:Lucas S M,1992:115—120.

OSMAN H, BLOSTEIN S, GAGNON L. Classification of ships in airborne sar imagery using back propagation neural networks [J]. SPIE Proc, 1997, 3161:126—136.

ASKARI F, ZERR B. Automatic approach to ship detection in spaceborne synthetic aperture radar imagery: an assessment of ship detection capability using RADARSAT . Technical Report SACLANTCEN-SR-338. La Spezia, Italy: SACLANT Undersea Research Centre, ,2000.36.

王隽, 杨劲松, 黄韦艮, 等. 基于极化SAR目标分解理论的船只几何结构初步分析 //第二届微波遥感技术研讨会. 北京: 中国空间科学学会, 2006:152—160.

种劲松. 合成孔径雷达图像舰船目标检测算法与应用研究 . 北京: 中国科学院电子学研究所,2002.

张晰. 星载SAR舰船目标探测实验研究[M]. 青岛: 中国海洋大学, 2008.

ZHANG Xi, ZHANG Jie, JI Yong-gang. Comparison and evaluation of ship target detection algorithms with SAR images. European Space Agency, (Special Publication) ESA SP, n 656 SP //Proceedings of SeaSAR 2008. Rome:ESA.2008: 4.

高贵,计科峰,匡纲要,等.高分辨率SAR图像目标峰值提取及峰值稳定性分析[J].电子与信息学报,2005, 27(4):561—565.

高贵,计科峰,匡纲要,等.高分辨率SAR图像目标峰值特征提取[J].信息处理,2005, 21(3):232—235.

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