Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

Lixia Wang; Jihong Pei; Weixin Xie; Jinyuan Liu

doi:10.1117/12.2283459

8 March 2018 Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

Lixia Wang, Jihong Pei, Weixin Xie, Jinyuan Liu

Author Affiliations +

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110B (2018) https://doi.org/10.1117/12.2283459
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

Citation Download Citation

Lixia Wang, Jihong Pei, Weixin Xie, and Jinyuan Liu "Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110B (8 March 2018); https://doi.org/10.1117/12.2283459

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