638 research outputs found

    Automatic refocus and feature extraction of single-look complex SAR signatures of vessels

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    In recent years, spaceborne synthetic aperture radar ( SAR) technology has been considered as a complement to cooperative vessel surveillance systems thanks to its imaging capabilities. In this paper, a processing chain is presented to explore the potential of using basic stripmap single-look complex ( SLC) SAR images of vessels for the automatic extraction of their dimensions and heading. Local autofocus is applied to the vessels' SAR signatures to compensate blurring artefacts in the azimuth direction, improving both their image quality and their estimated dimensions. For the heading, the orientation ambiguities of the vessels' SAR signatures are solved using the direction of their ground-range velocity from the analysis of their Doppler spectra. Preliminary results are provided using five images of vessels from SLC RADARSAT-2 stripmap images. These results have shown good agreement with their respective ground-truth data from Automatic Identification System ( AIS) records at the time of the acquisitions.Postprint (published version

    Sea target detection using spaceborne GNSS-R delay-doppler maps: theory and experimental proof of concept using TDS-1 data

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    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This study addresses a novel application of global navigation satellite system-reflectometry (GNSS-R) delay-Doppler maps (DDMs), namely sea target detection. In contrast with other competing remote sensing technologies, such as synthetic aperture radar and optical systems, typically exploited in the field of sea target detection, GNSS-R systems could be employed as satellite constellations, so as to fulfill the temporal requirements for near real-time ships and sea ice sheets monitoring. In this study, the revisit time offered by GNSS-R systems is quantitatively evaluated by means of a simulation analysis, in which three different realistic GNSS-R missions are simulated and analyzed. Then, a sea target detection algorithm from spaceborne GNSS-R DDMs is described and assessed. The algorithm is based on a sea clutter compensation step and uses an adaptive threshold to take into account spatial variations in the sea background and/or noise statistics. Finally, the sea target detector algorithm is tested and validated for the first time ever using experimental GNSS-R data from the U.K. TechDemoSat-1 dataset. Performance is assessed by providing the receiver operating characteristic curves, and some preliminary experimental results are presented.Peer ReviewedPostprint (published version

    Space-based Global Maritime Surveillance. Part I: Satellite Technologies

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    Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

    Ship Wake Detection in SAR Images via Sparse Regularization

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    In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page

    Ocean remote sensing techniques and applications: a review (Part II)

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    As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

    Ship Detection and Segmentation using Image Correlation

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    There have been intensive research interests in ship detection and segmentation due to high demands on a wide range of civil applications in the last two decades. However, existing approaches, which are mainly based on statistical properties of images, fail to detect smaller ships and boats. Specifically, known techniques are not robust enough in view of inevitable small geometric and photometric changes in images consisting of ships. In this paper a novel approach for ship detection is proposed based on correlation of maritime images. The idea comes from the observation that a fine pattern of the sea surface changes considerably from time to time whereas the ship appearance basically keeps unchanged. We want to examine whether the images have a common unaltered part, a ship in this case. To this end, we developed a method - Focused Correlation (FC) to achieve robustness to geometric distortions of the image content. Various experiments have been conducted to evaluate the effectiveness of the proposed approach.Comment: 8 pages, to be published in proc. of conference IEEE SMC 201

    High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection

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    Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called \textit{HSI-ShipDetectionNet} that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle marine ship detection dataset and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of recall, and mean average precision (mAP) while being lightweight and suitable for deployment on resource-limited platforms
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