Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

被引:30
作者
Xu, Fang [1 ,2 ]
Liu, Jinghong [1 ]
Dong, Chao [1 ,2 ]
Wang, Xuan [1 ]
机构
[1] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Key Lab Airborne Opt Imaging & Measurement, Changchun 130033, Jilin, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
关键词
remote sensing; ship detection; wavelet transform; the improved entropy; pixel distribution; OBJECT DETECTION; SALIENCY DETECTION; MODEL; SHAPE;
D O I
10.3390/rs9100985
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Ship detection by Unmanned Airborne Vehicles (UAVs) and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets are viewed as uncommon regions in the sea background caused by the differences in colors, textures, shapes, or other factors. Inspired by this fact, a global saliency model is constructed based on high-frequency coefficients of the multi-scale and multi-direction wavelet decomposition, which can characterize different feature information from edge to texture of the input image. To further reduce the false alarms, a new and effective multi-level discrimination method is designed based on the improved entropy and pixel distribution, which is robust against the interferences introduced by islands, coastlines, clouds, and shadows. The experimental results on optical remote sensing images validate that the presented saliency model outperforms the comparative models in terms of the area under the receiver operating characteristic curves core and the accuracy in the images with different sizes. After the target identification, the locations and the number of the ships in various sizes and colors can be detected accurately and fast with high robustness.
引用
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页数:19
相关论文
共 40 条
[1]  
Achanta R., 2010, P INT C IM PROC HONG
[2]  
[Anonymous], P IEEE INT C IM PROC
[3]  
[Anonymous], 2007, P IEEE C COMP VIS PA
[4]  
[Anonymous], 2006, Advances in Neural Information Processing Systems
[5]   A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images [J].
Bi, Fukun ;
Zhu, Bocheng ;
Gao, Lining ;
Bian, Mingming .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2012, 9 (04) :749-753
[6]   Ship Surveillance With TerraSAR-X [J].
Brusch, Stephan ;
Lehner, Susanne ;
Fritz, Thomas ;
Soccorsi, Matteo ;
Soloviev, Alexander ;
van Schie, Bart .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (03) :1092-1103
[7]   A survey on object detection in optical remote sensing images [J].
Cheng, Gong ;
Han, Junwei .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 117 :11-28
[8]   Multi-class geospatial object detection and geographic image classification based on collection of part detectors [J].
Cheng, Gong ;
Han, Junwei ;
Zhou, Peicheng ;
Guo, Lei .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 98 :119-132
[9]   Object detection in remote sensing imagery using a discriminatively trained mixture model [J].
Cheng, Gong ;
Han, Junwei ;
Guo, Lei ;
Qian, Xiaoliang ;
Zhou, Peicheng ;
Yao, Xiwen ;
Hu, Xintao .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2013, 85 :32-43
[10]   A complete processing chain for ship detection using optical satellite imagery [J].
Corbane, Christina ;
Najman, Laurent ;
Pecoul, Emilien ;
Demagistri, Laurent ;
Petit, Michel .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (22) :5837-5854