175 research outputs found

    Technique-Based Exploitation Of Low Grazing Angle SAR Imagery Of Ship Wakes

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    The pursuit of the understanding of the effect a ship has on water is a field of study that is several hundreds of years old, accelerated during the years of the industrial revolution where the efficiency of a shipโ€™s engine and hull determined the utility of the burgeoning globally important sea lines of communication. The dawn of radar sensing and electronic computation have expanding this field of study still further where new ground is still being broken. This thesis looks to address a niche area of synthetic aperture radar imagery of ship wakes, specifically the imaging geometry utilising a low grazing angle, where significant non-linear effects are often dominant in the environment. The nuances of the synthetic aperture radar processing techniques compounded with the low grazing angle geometry to produce unusual artefacts within the imagery. It is the understanding of these artefacts that is central to this thesis. A sub-aperture synthetic aperture radar technique is applied to real data alongside coarse modelling of a ship and its wake before finally developing a full hydrodynamic model for a shipโ€™s wake from first principles. The model is validated through comparison with previously developed work. The analysis shows that the resultant artefacts are a culmination of individual synthetic aperture radar anomalies and the reaction of the radar energy to the ambient sea surface and spike events

    Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico

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    Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platformsรขโ‚ฌโ„ข backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation

    ํ›ˆ๋ จ ์ž๋ฃŒ ์ž๋™ ์ถ”์ถœ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ธฐ๊ณ„ ํ•™์Šต์„ ํ†ตํ•œ SAR ์˜์ƒ ๊ธฐ๋ฐ˜์˜ ์„ ๋ฐ• ํƒ์ง€

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2021. 2. ๊น€๋•์ง„.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vesselโ€™s movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner. As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval. Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.์ „์ฒœํ›„ ์ง€๊ตฌ ๊ด€์ธก ์œ„์„ฑ์ธ SAR๋ฅผ ํ†ตํ•œ ์„ ๋ฐ• ํƒ์ง€๋Š” ํ•ด์–‘ ์ž์›์˜ ํ™•๋ณด์™€ ํ•ด์ƒ ์•ˆ์ „ ๋ณด์žฅ์— ๋งค์šฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ๊ณ„ ํ•™์Šต ๊ธฐ๋ฒ•์˜ ๋„์ž…์œผ๋กœ ์ธํ•ด ์„ ๋ฐ•์„ ๋น„๋กฏํ•œ ์‚ฌ๋ฌผ ํƒ์ง€์˜ ์ •ํ™•๋„ ๋ฐ ํšจ์œจ์„ฑ์ด ํ–ฅ์ƒ๋˜์—ˆ์œผ๋‚˜, ์ด์™€ ๊ด€๋ จ๋œ ๋‹ค์ˆ˜์˜ ์—ฐ๊ตฌ๋Š” ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ๋Ÿ‰์— ์ง‘์ค‘๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํƒ์ง€ ์ •ํ™•๋„์˜ ๊ทผ๋ณธ์ ์ธ ํ–ฅ์ƒ์€ ์ •๋ฐ€ํ•˜๊ฒŒ ์ทจ๋“๋œ ๋Œ€๋Ÿ‰์˜ ํ›ˆ๋ จ์ž๋ฃŒ ์—†์ด๋Š” ๋ถˆ๊ฐ€๋Šฅํ•˜๊ธฐ์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ ๋ฐ•์˜ ์‹ค์‹œ๊ฐ„ ์œ„์น˜, ์†๋„ ์ •๋ณด์ธ AIS ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜์˜ ์„ ๋ฐ• ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์‚ฌ์šฉ๋  ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ž๋™์ ์œผ๋กœ ์ทจ๋“ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ด์‚ฐ์ ์ธ AIS ์ž๋ฃŒ๋ฅผ SAR ์˜์ƒ์˜ ์ทจ๋“์‹œ๊ฐ์— ๋งž์ถ”์–ด ์ •ํ™•ํ•˜๊ฒŒ ๋ณด๊ฐ„ํ•˜๊ณ , AIS ์„ผ์„œ ์ž์ฒด๊ฐ€ ๊ฐ€์ง€๋Š” ์˜ค์ฐจ๋ฅผ ์ตœ์†Œํ™”ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ด๋™ํ•˜๋Š” ์‚ฐ๋ž€์ฒด์˜ ์‹œ์„  ์†๋„๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋„ํ”Œ๋Ÿฌ ํŽธ์ด ํšจ๊ณผ๋ฅผ ๋ณด์ •ํ•˜๊ธฐ ์œ„ํ•ด SAR ์œ„์„ฑ์˜ ์ƒํƒœ ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์œ„์„ฑ๊ณผ ์‚ฐ๋ž€์ฒด ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ •๋ฐ€ํ•˜๊ฒŒ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ๊ณ„์‚ฐ๋œ AIS ์„ผ์„œ์˜ ์˜์ƒ ๋‚ด์˜ ์œ„์น˜๋กœ๋ถ€ํ„ฐ ์„ ๋ฐ• ๋‚ด AIS ์„ผ์„œ์˜ ๋ฐฐ์น˜๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์„ ๋ฐ• ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ›ˆ๋ จ์ž๋ฃŒ ํ˜•์‹์— ๋งž์ถ”์–ด ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ทจ๋“ํ•˜๊ณ , ์–ด์„ ์— ๋Œ€ํ•œ ์œ„์น˜, ์†๋„ ์ •๋ณด์ธ VPASS ์ž๋ฃŒ ์—ญ์‹œ ์œ ์‚ฌํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ€๊ณตํ•˜์—ฌ ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ทจ๋“ํ•˜์˜€๋‹ค. AIS ์ž๋ฃŒ๋กœ๋ถ€ํ„ฐ ์ทจ๋“ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๋Œ€๋กœ ์ˆ˜๋™ ์ทจ๋“ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ์™€ ํ•จ๊ป˜ ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์‚ฌ๋ฌผ ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ์ •ํ™•๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์ทจ๋“ํ•œ ํ›ˆ๋ จ ์ž๋ฃŒ๋Š” ์ˆ˜๋™ ์ทจ๋“ํ•œ ํ›ˆ๋ จ ์ž๋ฃŒ ๋Œ€๋น„ ๋” ๋†’์€ ํƒ์ง€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์ด๋Š” ๊ธฐ์กด์˜ ์‚ฌ๋ฌผ ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ‰๊ฐ€ ์ง€ํ‘œ์ธ ์ •๋ฐ€๋„, ์žฌํ˜„์œจ๊ณผ F1 score๋ฅผ ํ†ตํ•ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ํ›ˆ๋ จ์ž๋ฃŒ ์ž๋™ ์ทจ๋“ ๊ธฐ๋ฒ•์œผ๋กœ ์–ป์€ ์„ ๋ฐ•์— ๋Œ€ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ๋Š” ํŠนํžˆ ๊ธฐ์กด์˜ ์„ ๋ฐ• ํƒ์ง€ ๊ธฐ๋ฒ•์œผ๋กœ๋Š” ๋ถ„๋ณ„์ด ์–ด๋ ค์› ๋˜ ํ•ญ๋งŒ์— ์ธ์ ‘ํ•œ ์„ ๋ฐ•๊ณผ ์‚ฐ๋ž€์ฒด ์ฃผ๋ณ€์˜ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ๋ถ„๋ณ„ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด์™€ ํ•จ๊ป˜, ์„ ๋ฐ• ํƒ์ง€ ๊ฒฐ๊ณผ์™€ ํ•ด๋‹น ์ง€์—ญ์— ๋Œ€ํ•œ AIS ๋ฐ VPASS ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ ๋ฐ•์˜ ๋ฏธ์‹๋ณ„์„ฑ์„ ํŒ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ ๋˜ํ•œ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1. Introduction - 1 - 1.1 Research Background - 1 - 1.2 Research Objective - 8 - Chapter 2. Data Acquisition - 10 - 2.1 Acquisition of SAR Image Data - 10 - 2.2 Acquisition of AIS and VPASS Information - 20 - Chapter 3. Methodology on Training Data Procurement - 26 - 3.1 Interpolation of Discrete AIS Data - 29 - 3.1.1 Estimation of Target Interpolation Time for Vessels - 29 - 3.1.2 Application of Kalman Filter to AIS Data - 34 - 3.2 Doppler Frequency Shift Correction - 40 - 3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 - 3.2.2 Mitigation of Doppler Frequency Shift - 48 - 3.3 Retrieval of Training Data of Vessels - 53 - 3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 - Chapter 4. Methodology on Object Detection Architecture - 66 - Chapter 5. Results - 74 - 5.1 Assessment on Training Data - 74 - 5.2 Assessment on AIS-based Ship Detection - 79 - 5.3 Assessment on VPASS-based Fishing Boat Detection - 91 - Chapter 6. Discussions - 110 - 6.1 Discussion on AIS-Based Ship Detection - 110 - 6.2 Application on Determining Unclassified Vessels - 116 - Chapter 7. Conclusion - 125 - ๊ตญ๋ฌธ ์š”์•ฝ๋ฌธ - 128 - Bibliography - 130 -Maste

    Oil spill and ship detection using high resolution polarimetric X-band SAR data

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    Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

    Literature review of the remote sensing of natural resources

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    Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

    Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

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    This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

    The Second Spaceborne Imaging Radar Symposium

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    Summaries of the papers presented at the Second Spaceborne Imaging Radar Symposium are presented. The purpose of the symposium was to present an overwiew of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans

    Polarimetric Synthetic Aperture Radar

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    This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

    Earth Resources: A continuing bibliography with indexes, issue 17

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    This bibliography lists 775 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1978. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis
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