154 research outputs found

    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

    Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation

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    Ship wake detection is of great importance in the characterisation of synthetic aperture radar (SAR) images of the ocean surface since wakes usually carry essential information about vessels. Most detection methods exploit the linear characteristics of the ship wakes and transform the lines in the spatial domain into bright or dark points in a transform domain, such as the Radon or Hough transforms. This paper proposes an innovative ship wake detection method based on sparse regularisation to obtain the Radon transform of the SAR image, in which the linear features are enhanced. The corresponding cost function utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution. The detection accuracy of the Cauchy prior based approach is 86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.Comment: 9 pages, 2 Figures and 2 Table

    On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty

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    Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of applications, including climate change, environmental monitoring, meteorology, high dimensional mapping, ship monitoring, or planetary exploration. In this paper, we investigate solutions to a number of inverse problems encountered in SAR imaging. We propose a convex proximal splitting method for the optimization of a cost function that includes a non-convex Cauchy-based penalty. The convergence of the overall cost function optimization is ensured through careful selection of model parameters within a forward-backward (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including super-resolution, image formation, and despeckling, as well as ship wake detection for maritime applications. The proposed method is compared to several methods employing classical penalty functions such as total variation (TVTV) and L1L_1 norms, and to the generalized minimax-concave (GMC) penalty. We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this paper.Comment: 18 pages, 7 figure

    A False-alarm-controllable Modified AdaBoost Wake Detection Method Using SAR Images

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    A false-alarm-controllable modified AdaBoost-based method is proposed for detecting ship wake from sea clutter in synthetic aperture radar (SAR) images. It reformulates the wake detection problem as a binary classification task in the multifeature space. The update strategy of the sample weights in the original AdaBoost is modified for wake detection. First, a detection result confidence factor is designed to deal with class imbalance between sea clutter and ship wake; then, the AdaBoost is further modified as a false alarm rate (FAR) controllable detector by introducing penalty parameters to adjust weights update strategies for the sea clutter. Meanwhile, the multifeature space is spanned by a novel frequency peak height ratio (FPHA) feature and four salient features. FPHA is proposed to enhance the separation between the wake and sea clutter, which is computed from the amplitude spectrum peak of the image after the Fourier transform. Experimental results show that the proposed detector can tackle the imbalanced data problem and flexibly control FAR by adjusting penalty parameters. Moreover, improved detection probability is also achieved compared with existing methods

    A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface

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    The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed-form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated.Comment: 6 pages, 2 Figures. This work has been presented in IGARSS 202

    Can We "Sense" the Call of The Ocean? Current Advances in Remote Sensing Computational Imaging for Marine Debris Monitoring

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    Especially due to the unconscious use of petroleum products, the ocean faces a potential danger: plasticΒ pollution\textit{plastic pollution}. Plastic pollutes not only the ocean but also directly the air and foods whilst endangering the ocean wild-life due to the ingestion and entanglements. Especially, during the last decade, public initiatives and academic institutions have spent an enormous time on finding possible solutions to marine plastic pollution. Remote sensing imagery sits in a crucial place for these efforts since it provides highly informative earth observation products. Despite this, detection, and monitoring of the marine environment in the context of plastic pollution is still in its early stages and the current technology offers possible important development for the computational efforts. This paper contributes to the literature with a thorough and rich review and aims to highlight notable literature milestones in marine debris monitoring applications by promoting the computational imaging methodology behind these approaches.Comment: 25 pages, 11 figure

    ν›ˆλ ¨ 자료 μžλ™ μΆ”μΆœ μ•Œκ³ λ¦¬μ¦˜κ³Ό 기계 ν•™μŠ΅μ„ ν†΅ν•œ SAR μ˜μƒ 기반의 μ„ λ°• 탐지

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    ν•™μœ„λ…Όλ¬Έ (석사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μžμ—°κ³Όν•™λŒ€ν•™ μ§€κ΅¬ν™˜κ²½κ³Όν•™λΆ€, 2021. 2. 김덕진.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 vessel’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.μ „μ²œν›„ 지ꡬ κ΄€μΈ‘ μœ„μ„±μΈ SARλ₯Ό ν†΅ν•œ μ„ λ°• νƒμ§€λŠ” ν•΄μ–‘ μžμ›μ˜ 확보와 해상 μ•ˆμ „ 보μž₯에 맀우 μ€‘μš”ν•œ 역할을 ν•œλ‹€. 기계 ν•™μŠ΅ κΈ°λ²•μ˜ λ„μž…μœΌλ‘œ 인해 선박을 λΉ„λ‘―ν•œ 사물 νƒμ§€μ˜ 정확도 및 νš¨μœ¨μ„±μ΄ ν–₯μƒλ˜μ—ˆμœΌλ‚˜, 이와 κ΄€λ ¨λœ λ‹€μˆ˜μ˜ μ—°κ΅¬λŠ” 탐지 μ•Œκ³ λ¦¬μ¦˜μ˜ κ°œλŸ‰μ— μ§‘μ€‘λ˜μ—ˆλ‹€. κ·ΈλŸ¬λ‚˜, 탐지 μ •ν™•λ„μ˜ 근본적인 ν–₯상은 μ •λ°€ν•˜κ²Œ μ·¨λ“λœ λŒ€λŸ‰μ˜ ν›ˆλ ¨μžλ£Œ μ—†μ΄λŠ” λΆˆκ°€λŠ₯ν•˜κΈ°μ—, λ³Έ μ—°κ΅¬μ—μ„œλŠ” μ„ λ°•μ˜ μ‹€μ‹œκ°„ μœ„μΉ˜, 속도 정보인 AIS 자료λ₯Ό μ΄μš©ν•˜μ—¬ 인곡 지λŠ₯ 기반의 μ„ λ°• 탐지 μ•Œκ³ λ¦¬μ¦˜μ— μ‚¬μš©λ  ν›ˆλ ¨μžλ£Œλ₯Ό μžλ™μ μœΌλ‘œ μ·¨λ“ν•˜λŠ” μ•Œκ³ λ¦¬μ¦˜μ„ μ œμ•ˆν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•΄ 이산적인 AIS 자료λ₯Ό SAR μ˜μƒμ˜ μ·¨λ“μ‹œκ°μ— λ§žμΆ”μ–΄ μ •ν™•ν•˜κ²Œ λ³΄κ°„ν•˜κ³ , AIS μ„Όμ„œ μžμ²΄κ°€ κ°€μ§€λŠ” 였차λ₯Ό μ΅œμ†Œν™”ν•˜μ˜€λ‹€. λ˜ν•œ, μ΄λ™ν•˜λŠ” μ‚°λž€μ²΄μ˜ μ‹œμ„  μ†λ„λ‘œ 인해 λ°œμƒν•˜λŠ” λ„ν”ŒλŸ¬ 편이 효과λ₯Ό λ³΄μ •ν•˜κΈ° μœ„ν•΄ SAR μœ„μ„±μ˜ μƒνƒœ 벑터λ₯Ό μ΄μš©ν•˜μ—¬ μœ„μ„±κ³Ό μ‚°λž€μ²΄ μ‚¬μ΄μ˜ 거리λ₯Ό μ •λ°€ν•˜κ²Œ κ³„μ‚°ν•˜μ˜€λ‹€. μ΄λ ‡κ²Œ κ³„μ‚°λœ AIS μ„Όμ„œμ˜ μ˜μƒ λ‚΄μ˜ μœ„μΉ˜λ‘œλΆ€ν„° μ„ λ°• λ‚΄ AIS μ„Όμ„œμ˜ 배치λ₯Ό κ³ λ €ν•˜μ—¬ μ„ λ°• 탐지 μ•Œκ³ λ¦¬μ¦˜μ˜ ν›ˆλ ¨μžλ£Œ ν˜•μ‹μ— λ§žμΆ”μ–΄ ν›ˆλ ¨μžλ£Œλ₯Ό μ·¨λ“ν•˜κ³ , 어선에 λŒ€ν•œ μœ„μΉ˜, 속도 정보인 VPASS 자료 μ—­μ‹œ μœ μ‚¬ν•œ λ°©λ²•μœΌλ‘œ κ°€κ³΅ν•˜μ—¬ ν›ˆλ ¨μžλ£Œλ₯Ό μ·¨λ“ν•˜μ˜€λ‹€. AIS μžλ£Œλ‘œλΆ€ν„° μ·¨λ“ν•œ ν›ˆλ ¨μžλ£ŒλŠ” κΈ°μ‘΄ λ°©λ²•λŒ€λ‘œ μˆ˜λ™ μ·¨λ“ν•œ ν›ˆλ ¨μžλ£Œμ™€ ν•¨κ»˜ 인곡 지λŠ₯ 기반 사물 탐지 μ•Œκ³ λ¦¬μ¦˜μ„ 톡해 정확도λ₯Ό ν‰κ°€ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό, μ œμ‹œλœ μ•Œκ³ λ¦¬μ¦˜μœΌλ‘œ μ·¨λ“ν•œ ν›ˆλ ¨ μžλ£ŒλŠ” μˆ˜λ™ μ·¨λ“ν•œ ν›ˆλ ¨ 자료 λŒ€λΉ„ 더 높은 탐지 정확도λ₯Ό λ³΄μ˜€μœΌλ©°, μ΄λŠ” 기쑴의 사물 탐지 μ•Œκ³ λ¦¬μ¦˜μ˜ 평가 μ§€ν‘œμΈ 정밀도, μž¬ν˜„μœ¨κ³Ό F1 scoreλ₯Ό 톡해 μ§„ν–‰λ˜μ—ˆλ‹€. λ³Έ μ—°κ΅¬μ—μ„œ μ œμ•ˆν•œ ν›ˆλ ¨μžλ£Œ μžλ™ 취득 κΈ°λ²•μœΌλ‘œ 얻은 선박에 λŒ€ν•œ ν›ˆλ ¨μžλ£ŒλŠ” 특히 기쑴의 μ„ λ°• 탐지 κΈ°λ²•μœΌλ‘œλŠ” 뢄별이 μ–΄λ €μ› λ˜ ν•­λ§Œμ— μΈμ ‘ν•œ μ„ λ°•κ³Ό μ‚°λž€μ²΄ μ£Όλ³€μ˜ μ‹ ν˜Έμ— λŒ€ν•œ μ •ν™•ν•œ 뢄별 κ²°κ³Όλ₯Ό λ³΄μ˜€λ‹€. λ³Έ μ—°κ΅¬μ—μ„œλŠ” 이와 ν•¨κ»˜, μ„ λ°• 탐지 결과와 ν•΄λ‹Ή 지역에 λŒ€ν•œ AIS 및 VPASS 자료λ₯Ό μ΄μš©ν•˜μ—¬ μ„ λ°•μ˜ 미식별성을 νŒμ •ν•  수 μžˆλŠ” κ°€λŠ₯μ„± λ˜ν•œ μ œμ‹œν•˜μ˜€λ‹€.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 - κ΅­λ¬Έ μš”μ•½λ¬Έ - 128 - Bibliography - 130 -Maste
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