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Article
Geochemistry & Geophysics
Xiangguang Leng et al.
Summary: In this article, a ship detection method for raw SAR echo data is proposed based on a nonimaging target sensing paradigm. By analyzing the one-dimensional sequence data, ships can be detected without the need for SAR imaging, opening up new possibilities for ship detection in the ocean.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Kai Han et al.
Summary: In this paper, efficient neural network designs for mobile devices are proposed. For CPU devices, a C-Ghost module is introduced to generate more feature maps, while for GPU devices, a G-Ghost stage structure is formulated to utilize stage-wise feature redundancy. Experimental results demonstrate the effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2022)
Article
Environmental Sciences
Runfan Xia et al.
Summary: This paper proposes a visual transformer framework based on contextual joint-representation learning for improving the accuracy of SAR image target detection. The framework combines the global contextual information perception of transformers and the local feature representation capabilities of CNNs. It introduces new modules and network designs to enhance the characterizability of multiscale SAR targets. Experimental results show that the proposed method achieves state-of-the-art accuracy.
Article
Engineering, Electrical & Electronic
Shuang Yang et al.
Summary: This paper introduces an improved fully convolutional one-stage object detector (Improved-FCOS) to address ship detection in synthetic aperture radar (SAR) images. The method proposes a multilevel feature attention mechanism, a feature refinement and reuse module, and a head improvement module to enhance the accuracy and robustness of ship detection. Experimental results demonstrate that Improved-FCOS achieves the best detection performance on multiple datasets.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Mingfeng Zh et al.
Summary: This letter proposes a novel ship detection model based on multifeature transformation and fusion (MFTF-Net) to address the issues of high false alarm detection rate and prone missed detection in SAR image ship detection. The proposed model utilizes anchor frame clustering, local enhancement network, improved transformer structure, and four-scale residual feature fusion network to achieve better performance compared to 13 baseline models.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Tianwen Zhang et al.
Summary: This paper proposes a full-level context squeeze-and-excitation ROI extractor (FL-CSE-ROIE) for synthetic aperture radar (SAR) ship instance segmentation. The method optimizes the feature extraction, context information, and squeeze-and-excitation process, and achieves satisfactory results on two datasets.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Zongyong Cui et al.
Summary: A ship detection method in large-scale SAR images via CenterNet is proposed, which defines the target as a point and locates the center point of the target through key point estimation to avoid missing small targets, and reduces false alarms through the introduction of SSE attention module. Experimental results show that the proposed method can detect all targets in dense-docking conditions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Computer Science, Information Systems
Shunjun Wei et al.
Article
Environmental Sciences
Sushil Kumar Joshi et al.
Article
Computer Science, Artificial Intelligence
Shaoqing Ren et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2017)
Article
Geochemistry & Geophysics
Gui Gao et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2009)