Synthetic Aperture Radar Ship Detection Using Capsule Networks

Schwegmann, C. P.; Kleynhans, W.; Salmon, B.P.; Mdakane, L.W.; Meyer, R. G. V.

doi:10.1109/igarss.2018.8517804

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…[21] Ship size, sea condition, accuracy, cost [37] Gradient explosion, robustness, speed, detection accuracy [22] Ship detection, efficiency, robustness, sea-land segmentation [38] Deep learning features, ship target, detection performance [26] Detection accuracy, false alarm rate, performance, position [39] Verification accuracy, testing accuracy, ship classification, false alarm [27] Detection rate, speed, detection accuracy, ship's target [40] Ship detection, ship size, performance, robustness [28] Real-time observation, rescue, detection accuracy, faster [41] Scene classification, ship detection, accuracy, efficiency [29] Missed detections, accuracy, densely arranged ships, scale sensitivity [42] Mean average precision, accuracy, dataset, performance [30] Multi-scene detection, false alarm, performance [43] Small targets, computational efficiency, detection performance, ship management [31] Training speed, accuracy, performance, ship detection [44] Extraction and classification of candidate regions, robustness, adaptability [32] Speed, accuracy, performance, ship detection, cost [45] Ship detection, image recognition, automatic, time [33] Lost ships, open-source, fast, cost [46] Small ships, computational efficiency, pixels, precision, classification [34] Accuracy, ship detection, mean average precision, unique [64] Processing speed, accuracy, object detection, unique [35] Detection, segmentation, accuracy, pixel level [65] Object detectors, land-ocean segmentation, performance [36] Automatic, accuracy, speed, loss function…”

Section: Features Citations Featuresmentioning

confidence: 99%

“…After testing and comparing it with other ML-based ship detection systems, it was proved that it enhances the accuracy of ship detection by 91.03% along with a false alarm rate of 9.5745 × 10 −9 . The performance can be further enhanced with fewer samples [26]. Wang et al [27] have conducted a study for the detection of the ship very efficiently and effectively by the employment of the enhanced YOLOv3 procedure.…”

Section: Introductionmentioning

confidence: 99%

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Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Yasir,

Niang,

Hossain

et al. 2023

JMSE

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We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

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Section: Features Citations Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Yasir,

Niang,

Hossain

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Its accuracy reached 98.14% in the tests of 10 kinds of MSTAR database. Schwegmann et al [39] applied CapsNet in the SAR ship detection task, which stimulated its ability to detect smaller adjacent ships. Comer et al [40] proposed a principal CapsNet (PCN) architecture for SAR image classification in the context of self-service learning (S 3 L).…”

Section: Introductionmentioning

confidence: 99%

MdpCaps-Csl for SAR Image Target Recognition With Limited Labeled Training Data

Hou

et al. 2020

IEEE Access

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Although convolutional neural networks (CNN) have shown excellent performance in many image recognition tasks, it commonly requires a lot of labeled data, and the recognition effect is frequently unsatisfied due to the limited labeled training data. In recent years, capsule network (CapsNet) has been shown to achieve a high recognition accuracy with a small group of training samples. In this study, a class separable loss based on cosine similarity is suggested to enhance the distinguishability of the extracted network. It is added as a regularization term to the original loss function to train the network, narrowing the intra-class difference and increasing the inter-class difference in each iteration. Meanwhile, a multidimensional parallel capsule module is established to obtain robust features and spatial relationships from the original images. Feature maps from convolution of different levels are extracted as the input of this module. Structural features derived from low-level convolution and semantic features derived from highlevel convolution are used for low-dimensional capsule coding and high-dimensional capsule coding, respectively. In our experiment, the general moving and stationary target acquisition and recognition (MSTAR) database is used. We find that the accuracy of the multi-dimensional parallel capsule network with class separable loss (MdpCaps-Csl) is 99.79% using all training samples, which is higher than most current recognition methods. More importantly, the accuracy is up to 97.73% even if only 10% training samples are applied, indicating MdpCaps-Csl can make excellent performance upon limited training samples. INDEX TERMS capsule network, class separable loss, image target recognition, limited labeled training data, multi-dimensional parallel capsule module, synthetic aperture radar.

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“…Jiao et al [30] detected multi-scale and multi-scene ship targets using densely connected network as backbone and introduced focal loss to faster RCNN structure. More recently, Wang et al [34] used RetinaNet [35] and Schwegmann et al [36] introduced Capsule Network to SAR ship detection. As most of these methods relied on the pretrained models on natural datasets to finetune the parameters of their models, Deng et al [37] designed a new detector using condensed backbone network and feature reuse strategy, which can be trained from scratch.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Feature Optimizing Network for Ship Detection in SAR Image

Zhang

Wang

et al. 2019

IEEE Access

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Deep learning-based methods have achieved great success in target detection tasks of computer vision, but when it comes to Synthetic Aperture Radar (SAR) image ship detection, some new challenges appear because of the wide swath of images, diverse appearances of ships and lack of detail information, which make the detection inefficient and less effective. Aiming to these issues, in this paper, a lightweight feature optimizing network (LFO-Net) based on popular single shot detector (SSD) model is proposed for single polarization SAR image ship detection. Firstly, a simpler structure called lightweight single shot detector (LSSD) is designed, which can be trained from scratch and can reduce the training and testing time without accuracy cost. Secondly, a new bi-directional feature fusion module including one semantic aggregation block and one feature reuse block is proposed to improve the performance of multi-scale targets detection by enhancing the features of both low feature layers and high feature layers. Then the features are further optimized by leveraging attention mechanism, which is beneficial to catch the silent information more efficiently. A set of experiments are implemented to verify the effectiveness of the proposed method using the public SAR ship detection dataset (SSDD). The results show that the proposed method has significant advantages in both speed and accuracy, and outperforms other state-of-art methods. Additionally, a test on GF-3 satellite SAR data with multiple modes verifies the generalization performance of this model.

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Synthetic Aperture Radar Ship Detection Using Capsule Networks

Cited by 13 publications

References 8 publications

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

MdpCaps-Csl for SAR Image Target Recognition With Limited Labeled Training Data

A Lightweight Feature Optimizing Network for Ship Detection in SAR Image

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