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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing images
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Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing images

机译：使用STFT海洋背景统计建模进行大规模海洋卫星遥感影像的船舶检测

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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 non-stationary 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.
机译：大范围的海洋遥感影像覆盖了大面积的海面，这种波动可以认为是一个非平稳过程。短时傅立叶变换（STFT）是适用于时变非平稳信号的分析工具。提出了一种基于二维STFT海面背景统计建模的大型海洋卫星遥感图像船舶探测方法。首先，本文将大尺度的海洋卫星遥感图像划分为小的子块，并将2-D STFT分别应用于每个子块。其次，研究了子区块的二维STFT光谱，发现了海洋背景与非海洋背景之间明显的不同特征。最后，给出了海面STFT频谱中所有有效频点的统计模型，并提出了基于二维STFT频谱建模的船舶检测方法。实验结果表明，该算法能有效地检测出具有较高召回率和失误率的舰船目标。

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