A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery

Shi, Hao; Zhang, Qingjun; Bian, Mingming; Wang, Hangyu; Wang, Zhiru; Chen, Liang; Yang, Jian

doi:10.3390/s18020563

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…Another type of method aligned the candidate regions with an estimated dominant orientation. Shi et al [13] adopted a radon transform method to rotate the patches in order to make all the candidates distributed in the same direction. Qi et al [14] performed the Principal Component Analysis (PCA) transform to obtain the direction of the main axis and rotated the ship candidates to the vertical direction before extracting HOG feature from the ship candidates.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Dong

Liu

et al. 2019

Remote Sensing

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Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

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Section: Introductionmentioning

confidence: 99%

Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Dong

Liu

et al. 2019

Remote Sensing

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“…Li et al construct a dataset named SAR Ship Detection Dataset (SSDD) to evaluate the performance of SAR ship detectors [42]. Besides the above-mentioned approaches, there are also several ship detectors for SAR images [43][44][45][46]. Generally, deep learning-based models adopt pooling and convolution operations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Proposal Generation for Ship Detection in SAR Images

et al. 2019

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The classic ship detection methods in synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. Generating a set of ship proposals before detection operation can effectively alleviate the multi-scale problem. In order to construct a scale-independent proposal generator for SAR images, we suggest four characteristics of ships in SAR images and the corresponding four procedures in this paper. Based on these characteristics and procedures, we put forward a framework to explore multi-scale ship proposals. The designed framework mainly contains two stages: hierarchical grouping and proposal scoring. Firstly, we extract edges, superpixels and strong scattering components from SAR images. The ship proposals are obtained at hierarchical grouping stage by combining the strong scattering components with superpixel grouping. Considering the difference of edge density and the completeness and tightness of contour, we obtain the scores to measure the confidence that a proposal contains a ship. Finally, the ranking proposals are obtained. Extensive experiments demonstrate the effectiveness of the four procedures. Our method achieves 0.70 the average best overlap (ABO) score, 0.59 the area under the curve (AUC) score and 0.85 best recall on a challenging dataset. In addition, the recall of our method on three scale subsets are all above 0.80. Experimental results demonstrate that our algorithm outperforms the approaches previously used for SAR images.

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“…Ship detection for synthetic aperture radar (SAR) images is of great importance in both military and commercial applications [ 1 , 2 , 3 ]. The technology of ship detection for SAR images has made great progress [ 4 , 5 , 6 ], while fewer works are available for inshore ship detection.…”

Section: Introductionmentioning

confidence: 99%

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

Xie

Zhang

Yang

et al. 2018

Sensors

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Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.

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A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery

Cited by 17 publications

References 33 publications

Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Multi-Scale Proposal Generation for Ship Detection in SAR Images

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

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