Sar ship dataset. In response to this challenge .

Sar ship dataset Improve this page Add a description, image, and links to the sar-ship-detection-dataset topic page so that developers can more easily learn about it. json file in MS COCO dataset format. HRSID dataset draws on the construction process of the Microsoft Common Objects in Context (COCO) datasets, including SAR images with different resolutions, polarizations, sea conditions, sea areas, and coastal ports. 0: A Sub-meter SAR Dataset for Fine-grained Ship Detection 原创 已于 2024-11-30 09:02:55 修改 · 1k 阅读 Oct 19, 2024 · Experimental evaluations conducted on the HRSID and SSDD datasets demonstrate that the proposed model achieves superior detection accuracy compared to other leading SAR ship detection methods. SL-SSDD: Sea-Land Segmentation Dataset for SSDD SL-SSDD is the first synergistic sea-land segmentation dataset tailored for deep learning-based SAR ship detection, built upon the well-established SAR Ship Detection Dataset (SSDD). In this paper, we propose a fusion attention based multi-scale sequence fusion SAR SAR-OOD-Detection-Data We have curated existing publicly available datasets, including vehicle targets (MSTAR), airplane targets (SAR-ACD), and ship targets (FUSAR-ship), and created a new dataset for SAR OOD detection. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. Similar to Official-SSDD, we randomly split the dataset into training (27,810 images), validation (3,972 images), and testing (7,947 images) sets using a 7:1:2 ratio. SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from synthetic aperture radar (SAR) imagery based on deep learning (DL). Most importantly, it encompasses SAR images in the L-, C-, and X-bands, which have not been provided by previous datasets. Apr 26, 2024 · The SSDD+ dataset is composed of 1160 SAR images containing 2456 ship targets, all of which are rotationally labelled in the SSDD+ dataset. Dataset images Feb 29, 2024 · YOLO detection - SAR ships data | Possible targets in large seascapes, improving search and rescue effectiveness. In low-illumination scenes, traditional ship detection algorithms often struggle due to poor visibility and blurred details in RGB video streams. 6 %/70. The methodology should be applicable for different resolution SAR data of same bands. 5% and 67. 5, 1 and 3 meters per pixel. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. Explore our data to track floating oil 1. Experiments show that this method can achieve better performance than the existing long tail classification methods on the SAR ship classification dataset. Undoubtedly, this situation reveals the Sep 15, 2020 · Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. SAR Ship Detection Dataset (SSDD) was released to the public in 2017, which was the first dataset of SAR ship image slices [5], which provides researchers with complete and standardized SAR ship About the program Umbra satellites offer the highest commercially available SAR imagery, surpassing 25 cm resolution. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. 0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from Automatic Identifica-tion System (AIS) and Google Earth. Jun 17, 2025 · The aim of the dataset is to introduce a single Synthetic Aperture Radar dataset which covers a wide range of possible requirements for scientific ship detection method analysis using the newest Synthetic Aperture Radar imagery available. Nevertheless, it still faces persistent challenges due to the small size of the ship, high noise level, multiple targets, and scale variations. The annotations of each instance are the corresponding bounding box and the ship’s outline. SAR data Here, we present a detailed introduction of the construction of the dataset, and show its two representative exemplary applications, namely SAR-optical image matching and SAR ship detection boosted by cross-modal information from optical images. In recent years, deep learning has made excellent progress in the field of SAR interpretation. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Each image tile Abstract:SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to re- search state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective . Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. To solve these problems, we propose a SAR ship detection model we released the SRSDD-v1. To boost the development of object detectors in SAR images, a SAR dataset is constructed. Apr 10, 2024 · SAR-Ship-Dataset: This dataset contains 43819 images from Gaofen-3 and Sentinel-1, all 256 × 256 pixels. 0 dataset are fourfold. Owing to the diversity of ship wake characteristics in SAR This example shows how to detect ships from Sentinel-1 C Band SAR Data using YOLOX object detection. SAR imagery provides data The dataset includes one ICEYE's Scan mode image of the Panama Canal, Panama. 59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Detecting ships in synthetic aperture radar (SAR) images is a challenging task due to various factors, such as the diverse distribution of ships and the intricate nature of SAR images. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. The page describes various aspects of the dataset including The technical paper detailing the dataset. com/CAESAR-Radi/SAR-Ship-Dataset Sep 12, 2024 · 以上就是对开源项目 SAR Ship Detection Dataset (SSDD) 的基本介绍，包括其目录结构、主要启动文件和配置文件的理解。 具体实现细节和高级功能需参考源码和项目文档中的进一步指示。 Deep learning has been widely used in the field of SAR ship detection. 95 respectively with 1. Synthetic aperture radar (SAR) remote sensing is an important method for marine monitoring due to its all-day, all-weather capability. Mar 12, 2024 · Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. Capable of capturing images day or night, through clouds, smoke, and rain, our technology enables all-weather monitoring. For example, SAR images are single-channel images in which ships are small and sparsely distributed. 9 % mask average precision (AP) in offshore scenes of SSDD/HRSID, and 56. 0), consisting of an equal number of ships and background classes. The experimental part shows that the proposed algorithm is able to achieve high detection accuracy with a very small number of parameters and computational complexity, realizing a good trade-off between algorithm lightness and detection accuracy. Oct 1, 2023 · This article describes a comprehensive Synthetic Aperture Radar (SAR) satellite based ships dataset for use in state of the art object detection algorithms. This approach ensures shorter re-imaging cycles and enables all-weather tracking. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques, and This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. Mar 14, 2025 · Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. Oct 13, 2017 · With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. This is a Large-Scale SAR Ship Detection Dataset-v1. Jan 16, 2024 · Finally, we validate the effectiveness of our method on three publicly available SAR ship detection datasets, SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). Dec 19, 2021 · To make full use of the polarization characteristics of multipolarized SAR, a dual-polarimetric SAR dataset specifically used for ship detection is presented in this paper (DSSDD). This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. However, most of the existing public SAR ship datasets are grayscale images under A dataset made by combining HRSID and OPEN-SSDD data, processed for ease of use With the development of imaging and space-borne satellite technology, a growing number of multipolarized SAR imageries have been implemented for object detection. Polarizations: VV and VH, fused into RGB channels for pseudo-color images. In order to increase the effectiveness of the model’s learning, we created a balanced subset of the Large Scale SAR Ship Detection Dataset (LS-SSDD-v1. Although many data augmentation methods—for example, clipping, pasting, and mixing—are used, the accuracy is improved little. According to our investigation, up to 46. Oct 9, 2024 · OpenSARWake A SAR ship wake rotation detection benchmark dataset. While most existing SAR ship research is primarily based on Convolutional Neural Networks (CNNs), and although deep learning advances SAR image Apr 7, 2024 · Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets May 1, 2025 · Extensive experiments on the SAR ship detection dataset (SSDD) and the high-resolution SAR image dataset (HRSID) demonstrate its superior performance. Limited availability of high-quality datasets hinders in-depth exploration of ship features in complex SAR images. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse SynthWakeSAR -> A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea, with paper SAR2Opt-Heterogeneous-Dataset -> SAR-optical images to be used as a benchmark in change detection and image transaltion on remote sensing images OpenSAR With the rapid development of advanced technologies, especially deep learning, we urgently need a large-scale dataset supporting deeper SAR image interpretation. 49M parameters. It can be used to develop object detectors for multi-scale and small object detection. [34] presented a lightweight model for ship detection and identification in complex SAR images, achieving excellent results on the SRSDDv1. This Abstract —This report describes the techniques and experiments for improving automatic ship detection from synthetic aperture radar (SAR) satellite imagery as a participant in the xView3 Dark Vessels Challenge 2021. To address these issues, this paper proposes a SAR ship classification Nov 3, 2025 · Ship detection plays an important role in port management, in terms of ship traffic, maritime rescue, cargo transportation and national defense. Experimental data from the SSDD, HRSID, and SAR-ship datasets indicate that LEAD-YOLO reduces parameter count by 55. All data in the dataset are from GF-3 Spotlight (SL) mode with a 1-m resolution and each image has 1024 × 1024 pixels, which is relatively larger Feb 13, 2024 · The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. 35% and increases detection frame rate by 57. We compared our method with other leading vessel detection methods in SAR A dataset made by combining HRSID and OPEN-SSDD data, processed for ease of use SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation. To facilitate network training, the large-scale images are directly This article describes a comprehensive Synthetic Aperture Radar (SAR) satellite based ships dataset for use in state of the art object detection algorithms. 929, and 0. Feb 1, 2024 · The current main SAR ship image datasets are SAR ship detection dataset (SSDD) [5] and high resolution SAR images dataset (HRSID) [6]. Achieves high accuracy on SSDD, HRSID, and SAR-ship datasets with reduced complexity. The images are derived from multi-scale ship slices from the RADARSAT-2, TerraSAR-X and Sentinel-1 satellites, with image resolutions ranging from 1 m to 15 m. 0) from Sentinel-1, for small ship detec-tion under large-scale backgrounds. Ai et al. The dataset comprises 11,590 image tiles containing 27,885 ships examples. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. This repository provides a comprehensive list of radar and optical satellite datasets curated for ship detection, classification, semantic segmentation, and instance segmentation tasks. Even if deep learning (DL) has led to an impressive performance boost on a variety of computer Jul 12, 2024 · SAR-Ship-Dataset数据集，用于遥感目标检测 源地址：https://github. It consists of 39,729 ship chips (remove some repeat clips) of 256 pixels in both range and azimuth. For construction, 50 dual-polarimetric Sentinel-1 SAR images were cropped into 1236 image slices with the size of 256 × 256 pixels. Ship detection in SAR images plays a critical role in shipwreck rescue, fishery and traffic management, and other marine applications. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. With the development of imaging and space-borne satellite technology, a growing number of multipolarized SAR imageries have been implemented for object detection. Ship Instances: 3540, labeled with rotatable (RBox) and horizontal bounding boxes Aug 17, 2022 · Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this letter, we construct a dataset specifically designed for ship detection in range-compressed SAR data, called RCShip-1. The extracted 5604 high-resolution SAR images contain 16951 ship instances. It consists of 1160 SAR images with average dimensions of 500×500 pixels. 2 %/42. Undoubtedly, this situation reveals the Jan 1, 2025 · In recent years, scholars and knowledgeable people from different regions globally have used data provided by the world's major satellites to produce many SAR ship datasets to meet research needs. 59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Detecting these ships is foundational for other downstream tasks, making the study of ship detection algorithms highly significant. Apr 30, 2024 · SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. This validation underscores the effectiveness of the algorithm in enhancing SAR ship detection capabilities. However, there is still a lack of reliable ship detection datasets that can satisfy the detection on the range-compressed domain. May 7, 2025 · Moreover, SAR ship datasets contain a high proportion of small targets [], which provide limited feature information [,] and are susceptible to background interference, severely constraining detection performance. 991, 0. - qingqing-zijin/LEAD-YOLO Dec 14, 2021 · However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. The xView3 Challenge provides a large multi-dimensional dataset of SAR satellite views to benchmark new approaches to automatically detect illegal fishing activities at a global This paper provides a SAR ship detection dataset with a high resolution and large-scale images, including 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in the loss of effective features of the target. It includes 1557 ship slices with multiple polarization modes and different resolutions, including 810 cargo ships, 505 tankers, and May 7, 2024 · This offline data augmentation strategy can address the scene imbalance problem and improve detection accuracy. This dataset contains a total of 5604 high-resolution SAR images and 16951 ship instances. The frame was taken on 7 February 2022, covering the entire Canal from the Pacific to the Atlantic, including ships on either side. : Over the recent years, deep-learning technology has been widely used. 962 on the SSDD, HRSID, and SAR-Ship-Dataset, respectively, with a model size of only 14. However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives and missed detections, posing challenges for lightweight and high-precision Feb 7, 2025 · The widespread application of synthetic aperture radar (SAR) in the domains of marine security and maritime traffic control ensures the criticality of ship detection. jpeg format with 24 bit color depth (8 bits per channel). This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes May 24, 2024 · SAR-Ship-Dataset的显著特点在于其大规模和多样性。该数据集不仅包含了39,729个船舶切片，且这些切片来自不同的卫星图像，具有丰富的背景和尺度变化。此外，所有船舶均被标注为同一类别，简化了分类任务，使得数据集更专注于检测任务。数据集的构建过程中，还特别注意了重复切片和边界框错误 SynthWakeSAR -> A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea, with paper SAR2Opt-Heterogeneous-Dataset -> SAR-optical images to be used as a benchmark in change detection and image transaltion on remote sensing images Feb 27, 2025 · Subsequently, as more SAR ship detection training datasets became available, researchers shifted their focus towards end-to-end SAR image ship detection methods that are entirely based on CNNs. LS-SSDD-v1. The annotations of each SAR image constitute a . This dataset was created specifically for use in machine learning research into SAR ship detection using machine learning and other techniques. Current deep-learning-based ship wake detection methods rely on supervised learning. To solve these problems, we propose a SAR ship detection model Sep 6, 2022 · The existing synthetic aperture radar (SAR) ship datasets have an imbalanced number of inshore and offshore ship targets, and the number of small, medium and large ship targets differs greatly. Mar 14, 2025 · Abstract Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. ##1. Unfortunately, due to the lack of a large volume of labeled datasets, object detectors for SAR ship detection have developed slowly. Specifically, DiffSARShipInst achieves up to 70. We use the LS-SSDD-v1. We analyze how approaches such as Contribute to CAESAR-Radi/SAR-Ship-Dataset development by creating an account on GitHub. [35] proposed a novel ship detection algorithm based on multi-scale rotationally invariant haar-like feature integrated CNN for multi-target environments in SAR images. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46,080 images for ten different real vessel models. Currently, ship detection primarily relies on deep learning algorithms, and training neural networks requires a large amount of data. The experiments conducted on SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset (LS-SSDD) demonstrate the effectiveness of the proposed method in improving detection performance. Jul 12, 2024 · SAR-Ship-Dataset数据集，用于遥感目标检测 源地址：https://github. 6% in 𝐦𝐀𝐏. 4 giga floating point operations. SAR data has advantages over optical data, as microwaves are capable of penetrating clouds and can be used in all types of weather. gz compressed dataset in JSON format with an accompanying readme. Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. This dataset contains 15 large SAR images that are broken down into 9000 sub-images with varying presence of land, sea, and ships. 34M parameters and 44. SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. The YOLO (You Only Look Once) line of object detectors has expanded rapidly since Mar 29, 2019 · To boost the development of object detectors in SAR images, a SAR dataset is constructed. These ships mainly have distinct scales and backgrounds. This dataset can be the catalyst for the development of object detectors in SAR images without land-ocean segmentation, thus helping the dynamic monitoring of marine activities. The Open Data Program monitors 20+ global locations, leveraging SAR’s unique capabilities for change monitoring. The spatial resolutions of SAR images are from 1 to 15 meters per pixel. Abstract—Deep learning (DL) has become a central approach for ship classification using synthetic aperture radar (SAR) im-agery. Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective Nov 20, 2024 · Synthetic aperture radar is widely applied to ship detection due to generating high-resolution images under diverse weather conditions and its penetration capabilities, making SAR images a valuable data source. The above 1160 images are in . 50:. Compared with other existing SAR ship datasets, the unique advantages of our SRSDD-v1. These datasets are ideal for applications in computer vision, machine learning, remote sensing, and maritime Dataset Description DSSDD (Dual-polarimetric SAR Ship Detection Dataset) Content: 50 dual-polarimetric SAR images from Sentinel-1. To make full use of the polarization characteristics of multipolarized SAR, a dual-polarimetric SAR dataset specifically used for ship detection is presented in this paper (DSSDD). In response to this challenge We’re on a journey to advance and democratize artificial intelligence through open source and open science. LEAD-YOLO: A lightweight, efficient YOLOv5 adaptation for SAR ship detection, optimized for edge devices with FasterNet, RFCBAMConv, and C3_CA modules. At the same time, the existing SAR ship detection models in the application have a huge structure and require high computing resources. However, most of the existing public SAR ship datasets are grayscale images under Mar 23, 2025 · This method strikes a balance between computational efficiency and model complexity. 0) dataset. Undoubtedly, this situation Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 7米到25米，极化方式包括HH、HV、VH和VV，成像模式包括超精细条带模式、精细条带模式、全极化条带模式、条带扫描模式和干涉宽幅模式。该数据集场景包括港口、近岸、岛屿和远海，类型 Sep 6, 2022 · The existing synthetic aperture radar (SAR) ship datasets have an imbalanced number of inshore and offshore ship targets, and the number of small, medium and large ship targets differs greatly. 6 % mask AP in inshore scenes of SSDD/HRSID. Deployment-ready for platforms like Cambricon MLU220. This tool supports multiple popular datasets including HRSID, SAR-Ship-Datas SAR-Ship Computer Vision Model SAR Updated 2 years ago Use this Model Use this Dataset SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). Curate this topic To validate the superior performance of DCEA, we conduct extensive experiments on multiple public datasets, achieving mean average precision scores of 0. 0 (LS-SSDD-v1. In Feb 20, 2024 · Ship detection and recognition in Synthetic Aperture Radar (SAR) images are crucial for maritime surveillance and traffic management. Jun 19, 2024 · This is followed by the integration of features across different scales using an FPN + PAN structure. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the The SAR images is derived from SAR-Ship-Dataset. Mar 29, 2019 · A SAR ship detection dataset under complex backgrounds is constructed. Jan 13, 2023 · 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. Sep 2, 2024 · The experimental results on the SAR ship detection dataset (SSDD) and rotated ship detection dataset in SAR images (RSDD-SAR) show that our proposed module is effective, and, compared with the other arbitrary-direction target detectors, LSR-Det obtains higher AP 50 and F1 scores, while both the parameters and computation are lower than for the Mar 14, 2025 · Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. The tar. Nov 3, 2025 · Satellite imagery provides data with high spatial and temporal resolution, which is useful for ship detection. This survey comprehensively analyzes the diverse DL techniques employed in this domain. The above 1160 images is are in . The dataset was created using the “AssenSAR Image Simulator”, also available via the University of Bristol. 0 (range-compressed ship dataset). To build our inshore-offshore classifier, we trained on the Large-Scale SAR Ship Detection Dataset-v1. Jun 28, 2025 · Nowadays, object detection has become increasingly crucial in various Internet-of-Things (IoT) systems, and ship detection is an essential component of this field. The spatial resolutions of SAR images are 0. Nov 14, 2021 · Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). A synthetic aperture radar (SAR) is used to persistently monitor marine areas in all-weather conditions for excellent ship and wake identification. This collection provides 3,973 images containing two polarization modes and 4,096 instances. This integral dataset is composed of 39,729 ship chips cropped from 102 Chinese Gaofen-3 images and 108 Sentinel-1 with 256 by 256 pixels. Jun 27, 2025 · To address these limitations, we present the Hybrid Optical and Synthetic Aperture Radar (SAR) Ship Re-Identification Dataset (HOSS ReID dataset), designed to evaluate the effectiveness of ship tracking using low-Earth orbit constellations of optical and SAR sensors. Oct 23, 2024 · In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built The synthetic aperture radar (SAR) is a crucial tool for maritime observation, with ships being the main targets at sea. Satellite imagery provides data with high spatial and temporal resolution, which is useful for ship detection. Dataset images have This dataset was created to help facilitate meaningful comparisons between SAR ship detection and discrimination methods but also provides additional information about some of the ships within the dataset from matched AIS transmissions. May 22, 2019 · SAR-Ship-Dataset解决了在复杂海况与多变背景下，合成孔径雷达（SAR）图像中船舶检测的难题，为学术研究提供了高质量的数据支持，从而促进了船舶检测技术的发展，增强了海上安全监控能力。 To tackle these challenges, we establish a new benchmark dataset and an open-source method for large-scale SAR object detection. 0 dataset. Feb 23, 2022 · 本数据集包括SAR船舶检测切片近40000张，采用了国产高分3号卫星和欧空局Sentinel-1卫星数据。图像分辨率覆盖1. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques To address the issue of SAR data scarcity and weak generalization ability of the object detection algorithms, we propose a comprehensive multi-resolution satellite based SAR ship detection dataset. A unified tool for processing various SAR (Synthetic Aperture Radar) ship detection datasets into a standardized format. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is Jun 5, 2018 · A Synthetic Aperture Radar ship dataset for detection, discrimination, and analysis. Nov 30, 2024 · 高分辨率SAR船舶检测数据集：SDFSD-v1. Sep 19, 2024 · Xiong et al. The dataset contains six categories of ships. The details of this dataset is referred to This SAR ship dataset was created using 94 SAR images, which contains 44 Chinese Gaofen-3 images, 27 RADARSAT-2 images, 3 Sentinel-1A images, and 20 TerraSAR-X images. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. However, no publicly available large-scale SAR dataset is available to support this learning method. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five Mar 6, 2025 · 舰船斜框检测数据集 (Rotated Ship Detection Dataset in SAR Images, RSDD-SAR)，采用了国产高分3号卫星数据和欧空局TerraSAR-X卫星数据。 Nov 3, 2024 · We propose an efficient method for ship-related querying in SAR images by connecting an object detection network with a vision-language model, thereby eliminating the need for model fine-tuning or multimodal dataset construction. Models that focus on extracting global semantic information can effectively achieve balanced detection of multiscale A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds Yuanyuan Wang 1,2 , Chao Wang 1,2 , Hong Zhang 1,* , Yingbo Dong 1,2 and Sisi Wei 1,2 Feb 19, 2025 · Experiments on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID) demonstrate that RSNet achieves a strong balance between lightweight design and detection performance, surpassing many state-of-the-art detectors, reaching 72. These 1160 images were obtained from RadarSat-2, TerraSAR-X and Sentinel-1 satellites. Nov 26, 2024 · This study utilizes the open-source SAR-ship dataset provided by the Chinese Academy of Sciences, which contains ships of various shapes and sizes in relatively low-resolution SAR images 31. SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). OpenSARWake is a benchmark dataset built for ship wake detection. Develop software with following two features- Land water discrimination using SAR imagery Output detected ships as a vector file, with an estimate of the size of ship - krisskad/sar_ship_detection_dataset Dec 6, 2024 · In the paper, the SAR image ship detection dataset (SSDD) is used as the experimental dataset. However, natural images differ significantly from SAR images. To address this weakness, we create the Lowship dataset and propose the YOLO-SAR FUSAR-Ship, a high-resolution SAR ship dataset built by Professor Feng Xu's team at the Key Laboratory of Information Science of Electromagnetic Waves, Fudan University, is a standard ship target identification dataset based on the domestic GF-3 satellite. 0 open source SAR dataset to build and train a computer vision small vessel detection model which automatically A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds 该数据集以我国国产高分三号SAR数据和Sentinel-1 SAR数据为主数据源，共采用了102景高分三号和108景Sentinel-1 SAR图像构建高分辨率SAR船舶目标深度学习样本库。 目前，该深度学习样本库包含43819个船舶切片。 SAR ship detection dataset (SSDD) is used for training and testing the proposed SAR ship detection method based on Swin Transformer and Feature Enhancement Feature Pyramid Network (FEFPN). This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. Format: Cropped into 1236 image slices (256x256 pixels). This survey reviews 74 representative studies selected from 187 publications, categorizing them into a taxonomy with four main dimensions: (i) DL architectures, (ii) datasets, (iii) image augmentation, and (iv) learning techniques. This specific dataset includes 2358 ship instances. Currently, we present two public large-scale datasets OpenSARShip and OpenSARUrban dedicated to deeper interpretation of SAR Ship Imagery and urban Imagery respectively. 6% compared to YOLOv5s. This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques, and This SAR ship dataset was created using 94 SAR images, which contains 44 Chinese Gaofen-3 images, 27 RADARSAT-2 images, 3 Sentinel-1A images, and 20 TerraSAR-X images. 1 Description Synthetic aperture radar (SAR) provides high-resolution, all-day, all-weather satellite imagery, which has become one of the most important means for high-resolution ocean observation and is well suited to better understand the maritime domain. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets Contribute to liyiniiecas/A_Dual-polarimetric_SAR_Ship_Detection_Dataset development by creating an account on GitHub. Jun 5, 2022 · At present, most SAR image detector backbone needs to pre-train on the classification dataset of natural images, and then fine-tune on the ship detection dataset of SAR images (for example SSDD). Sep 15, 2021 · SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from synthetic aperture radar (SAR) imagery based on deep In addition, due to the serious category imbalance problem in the SAR ship classification datasets, we propose a conditional probability-based multi-experts ensemble method. Aug 3, 2022 · SynthWakeSAR dataset The synthetic ship classification dataset of synthetic aperture radar (SAR) images of the sea surface (SynthWakeSAR) includes 10 real ship models for a total of 46080 simulated images containing visible ship wakes. Sep 15, 2021 · SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). In order to solve Dec 19, 2021 · However, most of the existing public SAR ship datasets are grayscale images under single polarization mode. xilz eger aywqdkl cuqtf kimfk xwskg exuoab eqfwtty flxyn lvbrniki jnazt ayr odjdnx qqinqtd bbryhrq