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.

Temporal alterations in coastlines depict the significant changes in coastal areas, driven by both natural processes and human activities. For island nations, monitoring of the coastline is essential due to their vulnerability to such impacts. In this study, Funafuti Atoll, an archipelago of small and scattered islands around the capital of Tuvalu, is selected as the study region, and the aim is to extract coastlines of different islands and investigate coastal area changes between 2019 and 2023 using Sentinel-2 imagery. A simple linear iterative clustering-based superpixel segmentation and adaptive thresholding approach is employed for coastline extraction. Initially, superpixel segmentation is conducted to cluster 3-band image pixels into coherent regions, excluding the sea area. Subsequently, the normalized difference vegetation index (NDVI) is calculated, and the superpixels are used to obtain corresponding NDVI regions, on which adaptive Gaussian thresholding is applied to extract coastlines. Finally, the areas enclosed by the extracted coastline boundaries are utilized for change analysis. The results indicate that islands along the western rim of Funafuti exhibited significant alteration (an average decrease of −14.48%), whereas those along the eastern rim remained relatively stable due to the presence of coral rubble ridges and steep slopes. The change analysis revealed that from 2019 to 2020, approximately 15.1 hectares (ha) were eroded, resulting in a net area change rate of −4.14%. Between 2020 and 2021, erosion increased to 20.2 ha, yielding a net change of −7.75%. From 2021 to 2022, 13.2 ha were eroded, corresponding to a −1.74% change. From 2022 to 2023, a net gain of 10.3 ha occurred (+0.25%), primarily due to land reclamation along the lagoon-facing coast of Fongafale Island. Overall, all islands showed a decreasing area trend between 2019 and 2023, with an average net change of −12.97%. The coastal changes occurred along the sand-dominated coast with gentle slopes, possibly driven by the impact of tropical cyclones, prolonged swells, and coastal flooding, which act as the primary driving forces for the study region.

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.

Vessels can deactivate their Automatic Identification System (AIS) to operate undetected and potentially engage in illegal activities. To address this, satellite-based radio frequency (RF) data are increasingly being used for identifying such vessels. This study evaluates the matching accuracy among RF and AIS data based on the frequency and distance. RF data were acquired on 22 September, 25 September, and 7 December 2023. According to the frequency range, the dataset was separated into frequency-1 (3.024–3.077 GHz) and frequency-2 (9.3734–9.4249 GHz). Six distance thresholds (2 km, 3 km, 6 km, 8 km, 13 km, and 18 km) were employed for the matching process. The results depicted that the average matching rates were 95%, 92%, and 92% for the RF dataset on 22 September, 25 September, and 7 December, respectively. Additionally, the results revealed that the matching rates decreased with distance, e.g., for the RF dataset on 22 September, the average highest matching rate (47%) was found at a 2 km distance and the minimum matching rate (0.9%) was observed at an 18 km distance. Furthermore, the analysis delineated that frequency-2 consistently exceeded frequency-1, particularly at longer distances, showing a more stable trend in matching accuracy.

This study examined the association between radio frequency (RF) data and ships in a congested area, with a focus on the Busan Port region in South Korea. RF datasets consisting of one L-band, four S-band, and two X-band frequencies were used in conjunction with Automatic Identification System (AIS) and Small Fishing Vessel Tracking System (V-Pass) data collected during the corresponding RF data periods. A distance-based association approach was applied, and the RF–ship association ratio (RSAR) was estimated as the ratio between the number of AIS-reported vessels associated with RF data and the total number of AIS-reported vessels present within the time period. The results indicate low overall RSAR in the congested region, with 6.5% for L-band, 1.7–24.6% for S-band, and 7.7–17.2% for X-band. Under stable high-pressure conditions (101.4–102.2 kPa) and light breeze conditions (0.9–3.6 m/s), atmospheric impacts on the RSAR can be considered minimal. Moreover, to indicate the relationship between the acquired RF signal and vessel congestion, a congestion index (CI) was derived from AIS and V-Pass data using a spatial grid-based method. The CI density maps within the congested region indicate that the acquired RF signals exist dominantly in low-congested areas.