Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks

被引：15
|
作者：
Yu, Ye [1 ,2 ]
Ai, Hua [1 ]
He, Xiaojun [1 ,3 ]
Yu, Shuhai [3 ]
Zhong, Xing [1 ,4 ]
Lu, Mu [3 ]
机构：
[1] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100039, Peoples R China
[3] Chang Guang Satellite Technol Co Ltd, Changchun 130102, Jilin, Peoples R China
[4] Chang Guang Satellite Technol Co Ltd, Key Lab Satellite Remote Sensing Applicat Technol, Changchun 130039, Jilin, Peoples R China
来源：
IEEE ACCESS | 2018年 / 6卷
关键词：
Object detection; feature extraction; CNN; AdaBoost classifier; TRACKS; SHAPE;
D O I：
10.1109/ACCESS.2018.2881479
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The ship detection field faces many challenges due to the large-scale and high complexity of optical remote sensing images. Therefore, an innovative ship detection method that is simple, accurate, and stable is proposed in this paper. The algorithm consists of the following two steps: 1) the AdaBoost classifier, combined with Haar-like features, is used to rapidly extract candidate area slices, and 2) according to the characteristics of ships, a periphery-cropped network is designed for ship verification. Furthermore, we analyze the characteristics of ocean images to improve the contrast between the target and the background. Thus, an RGB spectrum-stretching method is proposed. Finally, we evaluate our method using spaceborne optical images from the Jilin-1 satellite, Google satellites, and the public dataset NWPU VHR-10. Our experimental results indicate that the proposed algorithm achieves a high detection rate.
引用
收藏
页码：71122 / 71131
页数：10
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