Deep Learning for SAR Ship Detection: Past, Present and Future

Li, Xin; Xu, Congan; Su, Hang; Long, Gui‐Lu

doi:10.3390/rs14112712

Cited by 67 publications

(40 citation statements)

References 259 publications

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“…This is mainly because this field is relatively new, with few achievements, and is difficult to carry out a review. At present, the review papers those are References [83][84][85][86] are summaries of SAR ship detection based on deep learning. Mao et al [83] compared the classical deep learning based detectors on SSDD.…”

Section: Related Workmentioning

confidence: 99%

“…But it only reviewed the papers that are related to SSDD, the papers using other public datasets are not reviewed. Li et al [86] finished the first comprehensive survey of SAR ship detection. It analyzed the past, present and future of this area by the 177 published papers.…”

Section: Related Workmentioning

confidence: 99%

“…Since then, a large number of datasets and algorithms have been proposed, further promoting the development of this field. Reference [10] reviewed these achievements comprehensively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

Li¹,

Chen²,

Cheng³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, deep learning has greatly promoted the development of SAR ship detection. But the detectors are usually heavy and computation intensive which hinder the usage on the edge. In order to solve this problem, a lot of lightweight networks and acceleration ideas are proposed. In this survey, we review the papers that about real-time SAR ship detection. We firstly introduce the model compression and acceleration methods. They are pruning, quantization, knowledge distillation, low-rank factorization, lightweight networks and model deployment. They are the source of innovation in real-time SAR ship detection. Then we summarize the real-time object detection methods. They are two-stage, single-stage, anchor free, trained from scratch, model compression and acceleration. Researchers in SAR ship detection usually learn from these ideas. We then spend a lot of content on the review of the 70 real-time SAR ship detection papers. The years, datasets, journals, deep learning frameworks, and hardwares are introduced firstly. After that, the 10 public datasets and the evaluation metrics are shown. Then, we survey the 70 papers according to anchor free, trained from scratch, YOLO series, CFAR+CNN, lightweight backbone, pruning, quantization, knowledge distillation and hardware deployment. The experimental results show that the algorithms have been greatly developed in speed and accuracy. In the end we pointed out the problems of the 70 papers and the directions to be studied in the future. Our work can enable researchers to quickly understand the research status in this field.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

Li¹,

Chen²,

Cheng³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…SAR's self-illumination capability ensures that they always produce high-quality images under any circumstance (Chang et al, 2019). SAR has been extensively employed in ship identification (Ma et al, 2018;Xu et al, 2021;Li et al, 2022;Yasir et al, 2022;Xiong et al 2022), oil spill identification (Yekeen et al, 2020;Wang et al, 2022), change detection (Gao et al, 2019;Chen and Shi, 2020;Zhang et al, 2020b;Wang et al, 2022), and other fields (Niedermeier et al, 2000;Baselice and Ferraioli, 2013). Because of its broad observation range, brief observation duration, great data timeliness, and high spatial resolution (Ouchi, 2013), SAR performing a significant role in ship identification.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale ship target detection using SAR images based on improved Yolov5

Yasir

Liu

et al. 2023

Front. Mar. Sci.

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Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

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“…Application of machine learning in remote sensing has been developed from the classification for land use or disaster related from optical imagery (Doblas et al, 2020). Besides, classification on amplitude SAR images also has been combined using machine learning approaches for analyzing forest condition or object detections (Lapini et al, 2020;Garg et al, 2021;Li et al, 2022). In terms of application on InSAR data, several machine learning approaches has been employed such as optimization parameter on source deformation using cluster algorithms (Lee and Kim, 2021), volcanic deformation detection (Ghosh et al, 2021;Milillo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Surface deformation simulation for InSAR detection using a machine learning approach on the hantangang river volcanic field: A case study on the orisan mountain

Fadhillah

Hakim

Park

et al. 2022

Front. Environ. Sci.

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Recent developments in remote sensing research have resulted in a large amount of variability in the data provided by researchers. Synthetic aperture radar (SAR) is a tool used to measure surface deformation and assess changes in the Earth’s surface. Here, we consider the usefulness of Interferometric Synthetic Aperture Radar (InSAR) in assessing past volcanic activity as a key to learning the characteristics of the deformation around a volcano. The Hantangang River volcanic field (HRVF) is a geoheritage site in the Korean Peninsula that has interesting geological characteristics. This volcanic field has formed along 110 km of the paleochannel of the Hantangang River. Since the eruptions occurred from 0.15 to 0.51 Ma, the source is limited, which has raised interest in the assessment of volcanic landforms. The recent integration of machine learning and InSAR processing has shown promising results for many purposes, such as classifying, modeling, and detecting surface deformation. To examine the future impact based on information from the past, we utilized a synthetic interferogram with the Okada model and transferred it to a machine learning algorithm. The synthetic interferogram was formed based on Sentinel-1 C-band satellite data to simulate the deformation phases. The orbital errors, the topographical data errors, and the atmospheric effect were also simulated and added to the synthetic interferogram to enrich the learning input. A convolutional neural network (CNN) trained with the unwrapped simulated interferogram data and its performance was evaluated. Our proposed method exhibits the capability to detect volcanic activity’s deformation patterns with synthetic interferogram data. The results show that an overall accuracy of more than 80% was achieved using the CNN algorithms on the validation dataset. This study is the first to use machine learning approaches for detecting prehistorical volcanic deformation and demonstrates potential techniques for developing an approach based on satellite imagery. In addition, this study has introduced the possibility of developing a rapid detection of surface deformation using InSAR data based on a machine learning approach.

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Deep Learning for SAR Ship Detection: Past, Present and Future

Cited by 67 publications

References 259 publications

A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

Multi-scale ship target detection using SAR images based on improved Yolov5

Surface deformation simulation for InSAR detection using a machine learning approach on the hantangang river volcanic field: A case study on the orisan mountain

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