Research Papers

Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery

[+] Author Affiliations
Haitao Lang

Beijing University of Chemical Technology, Department of Applied Physics, No. 15 Beisanhuan East Road, Beijing 100029, China

Engineering Research Center for Marine Telemetry, State Oceanic Administration and China National Space Administration, No. 6 Xianxialing Road, Qingdao 266061, China

Jie Zhang

Engineering Research Center for Marine Telemetry, State Oceanic Administration and China National Space Administration, No. 6 Xianxialing Road, Qingdao 266061, China

The First Institute of Oceanography, State Oceanic Administration, No. 6 Xianxialing Road, Qingdao 266061, China

Ting Zhang

Engineering Research Center for Marine Telemetry, State Oceanic Administration and China National Space Administration, No. 6 Xianxialing Road, Qingdao 266061, China

The First Institute of Oceanography, State Oceanic Administration, No. 6 Xianxialing Road, Qingdao 266061, China

Di Zhao

Beijing University of Chemical Technology, Department of Applied Physics, No. 15 Beisanhuan East Road, Beijing 100029, China

Junmin Meng

Engineering Research Center for Marine Telemetry, State Oceanic Administration and China National Space Administration, No. 6 Xianxialing Road, Qingdao 266061, China

The First Institute of Oceanography, State Oceanic Administration, No. 6 Xianxialing Road, Qingdao 266061, China

J. Appl. Remote Sens. 8(1), 083623 (May 22, 2014). doi:10.1117/1.JRS.8.083623
History: Received October 25, 2013; Revised April 16, 2014; Accepted April 24, 2014
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Abstract.  Ship surveillance by remote sensing technology has become a valuable tool for protecting marine environments. In recent years, the successful launch of advanced synthetic aperture radar (SAR) sensors that have high resolution and multipolarimetric modes has enabled researchers to use SAR imagery for not only ship detection but also ship category recognition. A hierarchical ship detection and recognition scheme is proposed. The complementary information obtained from multipolarimetric modes is used to improve both the detection precision and the recognition accuracy. In the ship detection stage, a three-class fuzzy c-means clustering algorithm is used to calculate the segmenting threshold for prescreening ship candidates. To reduce the false alarm rate (FAR), we use a two-step discrimination strategy. In the first step, we fuse the detection results from multipolarimetric channels to reduce the speckle noise, ambiguities, sidelobes, and other sources of interference. In the second step, we use a binary classifier, which is trained with prior data collected on ships and nonships, to reduce the FAR even further. In the ship category recognition stage, we concatenate texture-based descriptors extracted from multiple polarmetric channels to construct a robust ship representation for category recognition. Furthermore, we construct and release a ship category database with real SAR data. We hope that it can be used to promote investigations of SAR ship recognition in the remote sensing and related academic communities. The proposed method is validated by a comprehensive experimental comparison to the state-of-the-art methods. The validation procedure showed that the proposed method outperforms all of the competing methods by about 5% and 15% in terms of ship detection and recognition, respectively.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Haitao Lang ; Jie Zhang ; Ting Zhang ; Di Zhao and Junmin Meng
"Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery", J. Appl. Remote Sens. 8(1), 083623 (May 22, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083623


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