훈련 자료 자동 추출 알고리즘과 기계 학습을 통한 SAR 영상 기반의 선박 탐지

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 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.전천후 지구 관측 위성인 SAR를 통한 선박 탐지는 해양 자원의 확보와 해상 안전 보장에 매우 중요한 역할을 한다. 기계 학습 기법의 도입으로 인해 선박을 비롯한 사물 탐지의 정확도 및 효율성이 향상되었으나, 이와 관련된 다수의 연구는 탐지 알고리즘의 개량에 집중되었다. 그러나, 탐지 정확도의 근본적인 향상은 정밀하게 취득된 대량의 훈련자료 없이는 불가능하기에, 본 연구에서는 선박의 실시간 위치, 속도 정보인 AIS 자료를 이용하여 인공 지능 기반의 선박 탐지 알고리즘에 사용될 훈련자료를 자동적으로 취득하는 알고리즘을 제안하였다. 이를 위해 이산적인 AIS 자료를 SAR 영상의 취득시각에 맞추어 정확하게 보간하고, AIS 센서 자체가 가지는 오차를 최소화하였다. 또한, 이동하는 산란체의 시선 속도로 인해 발생하는 도플러 편이 효과를 보정하기 위해 SAR 위성의 상태 벡터를 이용하여 위성과 산란체 사이의 거리를 정밀하게 계산하였다. 이렇게 계산된 AIS 센서의 영상 내의 위치로부터 선박 내 AIS 센서의 배치를 고려하여 선박 탐지 알고리즘의 훈련자료 형식에 맞추어 훈련자료를 취득하고, 어선에 대한 위치, 속도 정보인 VPASS 자료 역시 유사한 방법으로 가공하여 훈련자료를 취득하였다. AIS 자료로부터 취득한 훈련자료는 기존 방법대로 수동 취득한 훈련자료와 함께 인공 지능 기반 사물 탐지 알고리즘을 통해 정확도를 평가하였다. 그 결과, 제시된 알고리즘으로 취득한 훈련 자료는 수동 취득한 훈련 자료 대비 더 높은 탐지 정확도를 보였으며, 이는 기존의 사물 탐지 알고리즘의 평가 지표인 정밀도, 재현율과 F1 score를 통해 진행되었다. 본 연구에서 제안한 훈련자료 자동 취득 기법으로 얻은 선박에 대한 훈련자료는 특히 기존의 선박 탐지 기법으로는 분별이 어려웠던 항만에 인접한 선박과 산란체 주변의 신호에 대한 정확한 분별 결과를 보였다. 본 연구에서는 이와 함께, 선박 탐지 결과와 해당 지역에 대한 AIS 및 VPASS 자료를 이용하여 선박의 미식별성을 판정할 수 있는 가능성 또한 제시하였다.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 - 국문 요약문 - 128 - Bibliography - 130 -Maste

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

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

Clustering of Marine Oil-Spill Extent Using Sentinel-1 Dual Polarimetric Scattering Spectrum

Oil spills pose a significant threat to the maritime ecosystem. Identifying an oil spill is vital to assess its spread and drift to nearby coastal areas. Synthetic aperture radar (SAR) sensors are viable for mapping and monitoring marine oil spills. This study proposes a new technique that utilizes the dual-polarimetric Sentinel-1 SAR data. The method is based on projecting the 2 × 2 covariance matrix onto distinct random realizations of the normalized scattering configuration. We then obtain the dual-polarimetric spectrum of the scattering-type parameter, θDP. The θDP spectrum is then used in the unsupervised K-means clustering technique to segment oil spills from the rest. The cluster findings are then compared to the accuracies obtained using the standard scattering-type parameters from the eigen-decomposition approach (VV, VH) intensities and Otsu thresholding of [H + α + A] parameter. We demonstrate the proposed approach by clustering marine oil-spill extent over parts of India, Kuwait, the UAE, and the Mediterranean Sea obtained by Sentinel-1 SAR images. We observed that the clustering accuracy of the proposed technique outperforms the ones obtained from the channel (i.e., VV and VH) intensities, Otsu thresholding of [H + α + A] parameter, and the eigen-decomposition-based method. The proposed approach improves the overall accuracy by ≈8% and ≈20%, respectively, over different study areas

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition