In this paper, we present a novel method for ship detection in optical satellite image based on the ReedXiaoli (RX) method and the principal component analysis network (PCAnet). The proposed method consists of the following three steps. First, the spatially adjacent pixels in optical image are arranged into a vector, transforming the optical image into a 3D cube image. By taking this process, the contextual information of the spatially adjacent pixels can be integrated to magnify the discrimination between ship and background. Second, the RX anomaly detection method is adopted to preliminarily extract ship candidates from the produced 3D cube image. Finally, real ships are further confirmed among ship candidates by applying the PCAnet and the support vector machine (SVM). Specifically, the PCAnet is a simple deep learning network which is exploited to perform feature extraction, and the SVM is applied to achieve feature pooling and decision making. Experimental results demonstrate that our approach is effective in discriminating between ships and false alarms, and has a good ship detection performance.
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This paper is supported by the National Science Foundation of China (No. 61601179, No. 61301255), the National Natural Science Fund of China for Distinguished Young Scholars (No. 613 25007), the National Natural Science Fund of China for International Cooperation and Exchanges (No. 61520106001), the Science and Technology Plan Projects Fund of Hunan Province (No. 2015WK3001), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20130161120041), and the China Postdoctoral Science Special Foundation (No. 2014T70768, No. 2013M531782).
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Shao, X., Li, H., Lin, H. et al. Ship Detection in Optical Satellite Image Based on RX Method and PCAnet. Sens Imaging 18, 21 (2017). https://doi.org/10.1007/s11220-017-0167-6
- Ship detection
- Optical satellite image
- RX method
- Deep learning network
- Support vector machine