Mr. Yi-Ting Chen Profile

Yi-Ting Chen

at National Taiwan Ocean Univ.

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Publications (2)

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Proceedings Article | 3 October 2024 Poster + Paper

Combining SAR and AIS to track oil discharge vessels using the improved U-Net

Lena Chang, Yi-Ting Chen, Ching-Min Cheng

Proceedings Volume 13143, 1314313 (2024) https://doi.org/10.1117/12.3027230

KEYWORDS: Synthetic aperture radar, Artificial intelligence, Pollution, Environmental monitoring, Deep learning

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With the rapid development of maritime transportation, marine environmental pollution has become more and more serious. In particular, oil leakage from ships is regarded as a major threat to marine environmental pollution. Synthetic Aperture Radar (SAR) imagery has become an important technology for marine environment monitoring because of its high resolution, wide coverage, and less influence by light and weather conditions. However, SAR images cannot provide realtime information about ships. The Automatic Identification System (AIS) can provide dynamic information about ships, such as real-time position, heading, speed, etc., helping to identify ships in the sea area. This study combined SAR imagery and AIS data to detect oil spills at sea and identify suspicious oil-discharging vessels. First, an improved U-Net model was adopted to detect oil spills and ships in SAR imagery. To achieve high detection performance, the U-Net was modified by using a lightweight MobileNetv3 backbone architecture, convolutional block attention module (CBAM), atrous spatial pyramid pooling (ASPP), and full-scale feature aggregation. Experimental results showed that the proposed U-Net model improved the detection accuracy of oil spills and reduced the misclassification between oil spills and look-alikes. Then, the AIS data corresponding to the SAR image was collected and the trajectories of ships passing near the SAR acquisition time can be screened out. The study compared AIS data with SAR detection results to look for the ship that was closest to the oil spill and whose navigation trajectory was almost parallel to the direction of the oil spill extension. Thus, it can be inferred that the ship was suspected of discharging oil pollution. Through experiments on oil pollution incidents, the effectiveness of combining SAR and AIS in tracking oil-discharging ships was verified.

Proceedings Article | 6 September 2019 Paper

Classification of polarimetric SAR data based on subspace projection segmentation

Lena Chang, Yi-Ting Chen, Yang-Lang Chang, Yu-Jen Chen

Proceedings Volume 11132, 111320Z (2019) https://doi.org/10.1117/12.2529007

KEYWORDS: Surface plasmons, Polarimetry, Image segmentation, Speckle, Synthetic aperture radar

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The fully polarimetric SAR (PolSAR) data offers four polarimetric modes (i.e., HH, HV, VH, VV) and have shown the ability to provide better interpretation than single polarization case, which led to high classification accuracy. In this study, an efficient classification method for PolSAR data based on a subspace projection segmentation (SPS) approach is proposed to improve the classification accuracy. Instead of performing the comparison of multi-dimensional (MD) polarimetric feature vectors, the SPS first transforms the MD polarization feature vectors into one-dimensional (1D) projection lengths by projecting the feature vectors onto one reference subspace which is chosen to maximize the separation of two types of data. After the transformation, any 1D thresholding technique, such as the Otsu’s thresholding, can be applied to perform segmentation efficiently, which results in the reduction of computation complexity in segmentation. The proposed SPS can divide the data into proper homogeneous regions, that is, PolSAR data with similar polarization features being grouped together into regions. In the study, the polarimetric feature vectors are extracted from the coherent/covariance matrices obtained by the polarimetric scattering information. In addition, the referenced projection subspace is selected based on the coherent/covariance matrices of PolSAR data. Finally, the performance of the proposed SPS method is validated by simulations on PolSAR data obtained by NASA Airborne Synthetic Aperture Radar System (AIRSAR) during the PacRim II project and Advanced Land Observing Satellite (ALOS). Simulation results show that the proposed approach can reduce the computational complexity more effectively than other existing methods, and also achieve good classification accuracy.

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