Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods.
Synthetic aperture radar (SAR) is an important microwave sensor that is capable of high-resolution imaging. Extracting valuable features from the SAR target imagery is one of crucial issues in SAR automatic target recognition (ATR). In this paper, we propose a new feature extraction method named 2-D principalcomponent-analysis-based 2-D neighborhood virtual points discriminant embedding (2DPCA-based 2DNVPDE) for SAR ATR. The SAR imagery is projected into the feature space by 2DPCA and 2DNVPDE in this approach. 2DPCA is able to preserve the global spatial structure of the original imagery, while 2DNVPDE establishes the spatial relationships of the neighborhoods to find the classification information from the neighborhoods of the samples. Hence, our method can extract powerful recognition information and represent the original image in low dimensions. Based on the MSTAR dataset, the experimental results show that the proposed method is able to achieve a higher recognition rate with a lower feature dimension over some existing SAR imagery feature extraction methods. Besides, it indicates that our method has a significant advantage in recognition performance and a lower sensitivity in statistical standpoint.Index Terms-Automatic target recognition (ATR), feature extraction, synthetic aperture radar (SAR).
Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking.
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