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Proceedings Paper

Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image
Author(s): Lixia Wang; Jihong Pei; Weixin Xie; Jinyuan Liu
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Paper 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.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110B (8 March 2018); doi: 10.1117/12.2283459
Show Author Affiliations
Lixia Wang, Shenzhen Univ. (China)
Jihong Pei, Shenzhen Univ. (China)
Weixin Xie, Shenzhen Univ. (China)
Jinyuan Liu, Shenzhen Univ. (China)


Published in SPIE Proceedings Vol. 10611:
MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Nong Sang; Jie Ma; Zhong Chen, Editor(s)

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