Korean coastal waterways are potentially the most dangerous zones for marine accidents owing to a high volume of traffic, irregular traffic patterns of fishing vessels, frequent ship encounters, and narrowness of navigable area because of offshore construction and port enlargement construction. However, there are only few marine traffic assessment models that can be referenced from other countries, and none of them contain a risk assessment model suitable for the situation regarding Korean waterways. Therefore, it is necessary to develop a quantitative assessment of marine traffic environments based on ship navigator’s consciousness. To develop a new, suitable assessment model for marine traffic, the internal and external elements of vessels, based on a questionnaire survey and simulation experiment, are considered. The ship’s internal static parameters, vessel type, length, and width, as well as mariner’s experience, license, and rank on board are determined for the risk value. Furthermore, the external dynamic elements are calculated by the results of the questionnaire, which addresses the ship’s encounter situations and distances. Through this potential assessment of risk model, we used function as navigation equipment onboard to prevent a collision accident, applied it as a solution of a Vessel Traffic System (VTS) decision-making support system, and used the vessel evacuation route to recover the vessel’s normal situation from an emergency.

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships.

The macroalgal bloom (MAB) is caused by brown algae forming a floating mat. Most of its parts stay below the water surface, unlike green algae; thus, its backscatter value becomes weaker in the synthetic aperture radar (SAR) images, such as Sentinel−1, due to the dampening effect. Thus, brown algae patches appear to be thin strands in contrast to green algae and their detection by using a global threshold, which is challenging due to a similarity between the MAB patch and the ship’s sidelobe in the case of pixel value. Therefore, a novel approach is proposed to detect the MAB from the Sentinel−1 image by eliminating the ship’s sidelobe. An individually optimized threshold is applied to extract the MAB and the ships with sidelobes from the image. Then, parameters are adjusted based on the object’s area information and the ratio of length and width to filter out ships with sidelobes and clutter objects. With this method, an average detection accuracy of 82.2% is achieved by comparing it with the reference data. The proposed approach is simple and effective for detecting the thin MAB patch from the SAR image.

Synthetic Aperture Radar (SAR) is an efficient tool for monitoring ships. However, the detection accuracy of small fishing vessels is insufficient due to radar characteristics and image quality. Therefore, this study proposes a polarimetric fusion method focused on enhancing the detection performance of small fishing vessels using Sentinel-1 Single Look Complex (SLC) images. A combination of VH and VV polarization channels (fusVH) was applied to minimize radio frequency interference and azimuth smearing in SAR for detecting merchant ships, and the reflection symmetry (sym) was generated exploiting the fusVH image. Afterward, when the difference between sym and fusVH was less than 7.2 (dB) and sym was greater than -18.03 (dB), the maximum values of sym and fusVH were used to enhance the pixels of potential small ships. Using both sym and fusVH, small fishing vessels can be separated from merchant ships, which can be easily detected using fusVH images. The results were validated using 14 scenes from 2021 to 2023, and the average matching results were calculated to be 0.84 for the probability of detection rate and 0.29 for the false alarm rate.