Zhou, H.; Chen, P.; Li, Y.; Wang, B. Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images. J. Mar. Sci. Eng.2023, 11, 2081.
Zhou, H.; Chen, P.; Li, Y.; Wang, B. Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images. J. Mar. Sci. Eng. 2023, 11, 2081.
Zhou, H.; Chen, P.; Li, Y.; Wang, B. Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images. J. Mar. Sci. Eng.2023, 11, 2081.
Zhou, H.; Chen, P.; Li, Y.; Wang, B. Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images. J. Mar. Sci. Eng. 2023, 11, 2081.
Abstract
Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.
Keywords
large-scene SAR image; occlusion targets and small target detection; multi-attention mechanism
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
