2 research outputs found
νλ ¨ μλ£ μλ μΆμΆ μκ³ λ¦¬μ¦κ³Ό κΈ°κ³ νμ΅μ ν΅ν SAR μμ κΈ°λ°μ μ λ° νμ§
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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