| 第一作者： | Jiang, Mingda |
|---|---|
| 英文第一作者： | Jiang, Mingda |
| 联系作者： | Gu, Lingjia |
| 英文联系作者： | Gu, Lingjia |
| 发表年度： | 2023 |
| 卷： | 61 |
| 摘要： | Compared with most ship detection methods for synthetic aperture radar (SAR) images, ship contour extraction can provide more of the detailed shape and edge information of an observed ship and play a significant role in sea surface monitoring and marine transportation. In this study, a joint ship contour extraction method (faster region convolutional neural network (R-CNN), fast nonlocal mean (FNLM) filter and ChanVese model (FFCV) method) was proposed to obtain detailed ship information from SAR images, including ship detection in complex scenes and contour extraction in target slices. First, Faster R-CNN was employed to slice ships from large-scene SAR images. Then, FNLM filtering was applied to denoise and enhance the structural information of the target slices. Finally, an optimized Chan-Vese model was proposed in this article, which can not only accurately extract the contour of the observed ship but also reduce the computation time of the model. The SAR ship detection dataset (SSDD) was selected and finely relabeled to evaluate the contour extraction performance. An evaluation index RN, including quantitative value and offset direction, was developed to evaluate the extraction accuracy of the target contour from the SAR images. Compared with the Mask R-CNN network, the average contour extraction accuracy index RN of the proposed FFCV method reached -0.002 on all the images in the SSDD dataset, and its results were closer to the real ship contours while maintaining the applicability to complex scenes. |
| 刊物名称： | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
| 参与作者： | Jiang, MD (Jiang, Mingda) [1] ; Gu, LJ (Gu, Lingjia) [1] ; Li, XF (Li, Xiaofeng) [2] ; Gao, F (Gao, Fang) [3] , [4] ; Jiang, T (Jiang, Tao) [2] |
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Ship Contour Extraction From SAR Images Based on Faster R-CNN and ChanVese Model
本站小编 Free考研考试/2024-01-13
Ship Contour Extraction From SAR Images Based on Faster R-CNN and ChanVese Model
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