154 research outputs found
Ship Wake Detection in SAR Images via Sparse Regularization
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
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
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
() and 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
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
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
Especially due to the unconscious use of petroleum products, the ocean faces
a potential danger: . 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
νλ ¨ μλ£ μλ μΆμΆ μκ³ λ¦¬μ¦κ³Ό κΈ°κ³ νμ΅μ ν΅ν SAR μμ κΈ°λ°μ μ λ° νμ§
νμλ
Όλ¬Έ (μμ¬) -- μμΈλνκ΅ λνμ : μμ°κ³Όνλν μ§κ΅¬νκ²½κ³ΌνλΆ, 2021. 2. κΉλμ§.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 vesselβs 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 μ²ν μ§κ΅¬ κ΄μΈ‘ μμ±μΈ SARλ₯Ό ν΅ν μ λ° νμ§λ ν΄μ μμμ ν보μ ν΄μ μμ 보μ₯μ λ§€μ° μ€μν μν μ νλ€. κΈ°κ³ νμ΅ κΈ°λ²μ λμ
μΌλ‘ μΈν΄ μ λ°μ λΉλ‘―ν μ¬λ¬Ό νμ§μ μ νλ λ° ν¨μ¨μ±μ΄ ν₯μλμμΌλ, μ΄μ κ΄λ ¨λ λ€μμ μ°κ΅¬λ νμ§ μκ³ λ¦¬μ¦μ κ°λμ μ§μ€λμλ€. κ·Έλ¬λ, νμ§ μ νλμ κ·Όλ³Έμ μΈ ν₯μμ μ λ°νκ² μ·¨λλ λλμ νλ ¨μλ£ μμ΄λ λΆκ°λ₯νκΈ°μ, λ³Έ μ°κ΅¬μμλ μ λ°μ μ€μκ° μμΉ, μλ μ λ³΄μΈ AIS μλ£λ₯Ό μ΄μ©νμ¬ μΈκ³΅ μ§λ₯ κΈ°λ°μ μ λ° νμ§ μκ³ λ¦¬μ¦μ μ¬μ©λ νλ ¨μλ£λ₯Ό μλμ μΌλ‘ μ·¨λνλ μκ³ λ¦¬μ¦μ μ μνμλ€.
μ΄λ₯Ό μν΄ μ΄μ°μ μΈ AIS μλ£λ₯Ό SAR μμμ μ·¨λμκ°μ λ§μΆμ΄ μ ννκ² λ³΄κ°νκ³ , AIS μΌμ μμ²΄κ° κ°μ§λ μ€μ°¨λ₯Ό μ΅μννμλ€. λν, μ΄λνλ μ°λ체μ μμ μλλ‘ μΈν΄ λ°μνλ λνλ¬ νΈμ΄ ν¨κ³Όλ₯Ό 보μ νκΈ° μν΄ SAR μμ±μ μν 벑ν°λ₯Ό μ΄μ©νμ¬ μμ±κ³Ό μ°λ체 μ¬μ΄μ 거리λ₯Ό μ λ°νκ² κ³μ°νμλ€. μ΄λ κ² κ³μ°λ AIS μΌμμ μμ λ΄μ μμΉλ‘λΆν° μ λ° λ΄ AIS μΌμμ λ°°μΉλ₯Ό κ³ λ €νμ¬ μ λ° νμ§ μκ³ λ¦¬μ¦μ νλ ¨μλ£ νμμ λ§μΆμ΄ νλ ¨μλ£λ₯Ό μ·¨λνκ³ , μ΄μ μ λν μμΉ, μλ μ λ³΄μΈ VPASS μλ£ μμ μ μ¬ν λ°©λ²μΌλ‘ κ°κ³΅νμ¬ νλ ¨μλ£λ₯Ό μ·¨λνμλ€.
AIS μλ£λ‘λΆν° μ·¨λν νλ ¨μλ£λ κΈ°μ‘΄ λ°©λ²λλ‘ μλ μ·¨λν νλ ¨μλ£μ ν¨κ» μΈκ³΅ μ§λ₯ κΈ°λ° μ¬λ¬Ό νμ§ μκ³ λ¦¬μ¦μ ν΅ν΄ μ νλλ₯Ό νκ°νμλ€. κ·Έ κ²°κ³Ό, μ μλ μκ³ λ¦¬μ¦μΌλ‘ μ·¨λν νλ ¨ μλ£λ μλ μ·¨λν νλ ¨ μλ£ λλΉ λ λμ νμ§ μ νλλ₯Ό 보μμΌλ©°, μ΄λ κΈ°μ‘΄μ μ¬λ¬Ό νμ§ μκ³ λ¦¬μ¦μ νκ° μ§νμΈ μ λ°λ, μ¬νμ¨κ³Ό F1 scoreλ₯Ό ν΅ν΄ μ§νλμλ€. λ³Έ μ°κ΅¬μμ μ μν νλ ¨μλ£ μλ μ·¨λ κΈ°λ²μΌλ‘ μ»μ μ λ°μ λν νλ ¨μλ£λ νΉν κΈ°μ‘΄μ μ λ° νμ§ κΈ°λ²μΌλ‘λ λΆλ³μ΄ μ΄λ €μ λ νλ§μ μΈμ ν μ λ°κ³Ό μ°λ체 μ£Όλ³μ μ νΈμ λν μ νν λΆλ³ κ²°κ³Όλ₯Ό 보μλ€. λ³Έ μ°κ΅¬μμλ μ΄μ ν¨κ», μ λ° νμ§ κ²°κ³Όμ ν΄λΉ μ§μμ λν AIS λ° VPASS μλ£λ₯Ό μ΄μ©νμ¬ μ λ°μ λ―Έμλ³μ±μ νμ ν μ μλ κ°λ₯μ± λν μ μνμλ€.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 μμ½λ¬Έ - 128 -
Bibliography - 130 -Maste
- β¦