Sea Surface Target Detection Based on Multi-dimensional Feature Fusion
To improve the detection probability of sea surface targets,a multi-dimensional feature fusion method for target detection is proposed.Firstly,based on the time domain,two features:Hurst exponent and information entropy(IE)are extracted.Secondly,based on the frequency do-main,the frequency variance to average ratio(FVAR)is extracted as a feature.Then,based on the time-frequency domain,the time-frequency map after short time fourier transform(STFT)is ex-tracted as the feature.Finally,two improved convolutional neural networks(CNNs)are used to train one-dimensional and two-dimensional features respectively.The two networks are then com-bined to obtain the final target detection result.Further comparison with traditional convolutional neural networks shows that the proposed method reduces computation time by more than 40％un-der the same training conditions.
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