The sea background often fluctuates violently and has a low contrast with the target, which brings difficulties in detecting the infrared maritime targets. To solve this problem, the mixture Gaussian background modeling for sea background in the Fourier domain (FGMM) was proposed. First, the mixture Gaussian background model was constructed for the amplitude spectrum sequence at each frequency point. Second, the amplitude spectrum of the test frame was compared with the mixture Gaussian background model to separate the background and foreground frequency points. And the parameters of each Gaussian distribution were updated to adapt to the change of seawater. Also, the two features of the neighborhood amplitude spectrum contrast and the information entropy of local amplitude spectrum were fused into the mixture Gaussian background model to get the final detection results. Experimental results showed that the proposed method has good effects in suppressing the seawater and detecting the targets. Moreover, compared with the traditional spatial mixture Gaussian background modeling algorithm, its performance has been significantly improved.
For ship targets detection in cluttered infrared image sequences, a robust detection method, based on the probabilistic single Gaussian model of sea background in Fourier domain, is put forward. The amplitude spectrum sequences at each frequency point of the pure seawater images in Fourier domain, being more stable than the gray value sequences of each background pixel in the spatial domain, are regarded as a Gaussian model. Next, a probability weighted matrix is built based on the stability of the pure seawater’s total energy spectrum in the row direction, to make the Gaussian model more accurate. Then, the foreground frequency points are separated from the background frequency points by the model. Finally, the false-alarm points are removed utilizing ships’ shape features. The performance of the proposed method is tested by visual and quantitative comparisons with others.
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