Open AccessShip Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks
Author(s) -
Ye Yu,
Hua Ai,
Xiaojun He,
Shuhai Yu,
Xing Zhong,
Mu Lu
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2881479
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.
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