Ship Detection Method Based on Frequency Enhanced MSER For High Resolution SAR Image

2021 ◽  
Author(s):  
Kun Sun ◽  
Liang Ma ◽  
Fei Wang ◽  
Yi Liang
Keyword(s):  
High Resolution ◽  
Detection Method ◽  
Sar Image ◽  
Ship Detection

scholarly journals Hierarchical ship detection method for space-borne SAR image

2019 ◽  
Vol 2019 (21) ◽  
pp. 7662-7666
Author(s):  
Wei Tang ◽  
Baojun Zhao ◽  
Linbo Tang ◽  
Jinghong Nan
Keyword(s):  
Detection Method ◽  
Sar Image ◽  
Ship Detection

scholarly journals Two-Parameter CFAR Ship Detection Algorithm Based on Rayleigh Distribution in SAR Images

2021 ◽  
Author(s):  
Ruochen Wu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Microwave Remote Sensing ◽  
Rayleigh Distribution ◽  
Global Ocean ◽  
Distribution Model ◽  
Sar Image ◽  
Sar Images ◽  
Ship Detection ◽  
Two Parameter

Synthetic Aperture Radar (SAR) is an active type of microwave remote sensing. Using the microwave imaging system, remote sensing monitoring of the land and global ocean can be done in any weather conditions around the clock. Detection of SAR image targets is one of the main needs of radar image interpretation applications. In this paper, an improved two-parameter CFAR algorithm based on Rayleigh distribution and morphological processing is proposed to perform ship detection and recognition in high resolution SAR images. Through simulation experiments, comprehensive study of the two algorithms for high resolution SAR image target detection is achieved. Finally, the results of ship detection experiments are compared and analyzed, and the effects of detection are evaluated according to the Rayleigh distribution model and algorithms.

scholarly journals Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet

2020 ◽  
Vol 12 (1) ◽  
pp. 167 ◽  
Author(s):  
Shunjun Wei ◽  
Hao Su ◽  
Jing Ming ◽  
Chen Wang ◽  
Min Yan ◽  
...  
Keyword(s):  
High Resolution ◽  
Detection Performance ◽  
Evaluation Metrics ◽  
Sar Image ◽  
Low Resolution ◽  
Feature Maps ◽  
Ship Detection ◽  
Feature Pyramid ◽  
Sar Imagery ◽  
Performance Gains

Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a challenging problem in the case of complex environments, especially inshore and offshore scenes. Nowadays, the existing methods of SAR ship detection mainly use low-resolution representations obtained by classification networks or recover high-resolution representations from low-resolution representations in SAR images. As the representation learning is characterized by low resolution and the huge loss of resolution makes it difficult to obtain accurate prediction results in spatial accuracy; therefore, these networks are not suitable to ship detection of region-level. In this paper, a novel ship detection method based on a high-resolution ship detection network (HR-SDNet) for high-resolution SAR imagery is proposed. The HR-SDNet adopts a novel high-resolution feature pyramid network (HRFPN) to take full advantage of the feature maps of high-resolution and low-resolution convolutions for SAR image ship detection. In this scheme, the HRFPN connects high-to-low resolution subnetworks in parallel and can maintain high resolution. Next, the Soft Non-Maximum Suppression (Soft-NMS) is used to improve the performance of the NMS, thereby improving the detection performance of the dense ships. Then, we introduce the Microsoft Common Objects in Context (COCO) evaluation metrics, which provides not only the higher quality evaluation metrics average precision (AP) for more accurate bounding box regression, but also the evaluation metrics for small, medium and large targets, so as to precisely evaluate the detection performance of our method. Finally, the experimental results on the SAR ship detection dataset (SSDD) and TerraSAR-X high-resolution images reveal that (1) our approach based on the HRFPN has superior detection performance for both inshore and offshore scenes of the high-resolution SAR imagery, which achieves nearly 4.3% performance gains compared to feature pyramid network (FPN) in inshore scenes, thus proving its effectiveness; (2) compared with the existing algorithms, our approach is more accurate and robust for ship detection of high-resolution SAR imagery, especially inshore and offshore scenes; (3) with the Soft-NMS algorithm, our network performs better, which achieves nearly 1% performance gains in terms of AP; (4) the COCO evaluation metrics are effective for SAR image ship detection; (5) the displayed thresholds within a certain range have a significant impact on the robustness of ship detectors.

scholarly journals S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 423-430 ◽  
Author(s):  
Ruiqian Zhang ◽  
Jian Yao ◽  
Kao Zhang ◽  
Chen Feng ◽  
Jiadong Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Head Model ◽  
Shape Features ◽  
Remote Sensing Images ◽  
Line Segments ◽  
Ship Detection ◽  
Efficient Detection

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

scholarly journals A novel Ship detection method from SAR image with reduced false alarm

2021 ◽  
Vol 1817 (1) ◽  
pp. 012010
Author(s):  
J Anil Raj ◽  
Sumam Mary Idicula ◽  
Binu Paul
Keyword(s):  
False Alarm ◽  
Detection Method ◽  
Sar Image ◽  
Ship Detection

scholarly journals S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 423-430 ◽  
Author(s):  
Ruiqian Zhang ◽  
Jian Yao ◽  
Kao Zhang ◽  
Chen Feng ◽  
Jiadong Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Head Model ◽  
Shape Features ◽  
Remote Sensing Images ◽  
Line Segments ◽  
Ship Detection ◽  
Efficient Detection

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

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