In 2021, compared to the last year, global port container logistic volumes increased by 7% (857 million 20-foot equivalent units (TEU)), while deliveries of newly built container ships increased by 18% (10,929 thousand gross tons) [1]. Hence, the problem instigated by an increased volume of logistics and port congestion can be solved through the efficient operation of the port, and ship detection and volume data processing have become important. Ship monitoring related to port logistics is possible by checking the Auto Identification System (AIS) [2]. The Terrestrial AIS (T-AIS) system is capable of detecting only short-range communication up to 50 nautical miles [3], and there is an omission to monitor the ship when the navigator controls the transmission-receiving of AIS [4]. Therefore, as the ability of satellites increases, there is a movement to detect missing ships using satellite images [5]. In this study, ship detection was conducted to monitor container ships that were not operate in the anchorage area around the port or berth at the pier. In order to find container ships through deep learning, four types of vessels (container ships, bulk ships, oil tankers, and small ships) were extracted from Compact Advanced Satellite 500-1 (CAS500-1) satellite images in the yellow sea and one port to make a training dataset. The CAS500 satellites carry different sensors for different Earth observation missions. CAS500-1 has the panchromatic and multispectral modes using the AEISS-C (Advanced Earth Imaging Sensor System) payload for land surface imagery, land topography imagery, and vegetation-type imagery. Although improvement in remote surface reflectance is needed, it can be beneficial at sea. The detection model was established through YOLOv5 (You Only Look Once version 5) [6] deep learning algorithm. The model's performance was evaluated by applying them to the CAS500-1, and Korea Multi-Purpose Satellite-3 (KOMPSAT-3), taken at the Busan New Port, Korea, and Oakland Port in the U.S. The validation was performed based on mated YOLO results and AIS. In this process, the ship type, which is static information of the ship, and the position value, which is dynamic information, were compared. Ship detection results presented an accuracy of 66% to 93%, and the classification results had an accuracy of 70% to 89%. In particular, the container ships interested in this study were accurately classified as container ships of at least 85% up to 100%. This study implies that it attempts to classify vessel types in satellite images using deep learning and evaluates the possibility of ship detection and ship classification for types based on the CAS500-1 satellite image. This study concludes that if a deep learning model is applied to the CAS500-1 and multi satellite to detect the ship's coordinate and type, it can contribute to the management of shipping traffic and monitoring of ship traffic for autonomous driving ships. Also, the results indicate that high resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

This paper presents a novel approach for detecting oil spills, employing two distinct methods. The first method involves utilizing an Oil Spill Index (OSI) that analyzes spectral band information, while the second method utilizes a Deep Learning (DL) segmentation model. The outcomes of both methods are then combined to generate training data, and this iterative process is repeated to update the oil spill DL model. The training data used in this approach are obtained from multiple remote sensing platforms including optical and SAR satellite imagery. By employing this proposed method, it is anticipated that high-quality and comprehensive training datasets will be generated, enabling the DL model to perform effectively.

Various platforms, such as satellites, manned aircraft, Unmanned Aerial Vehicle (UAV), and mobile devices, can be used to obtain remotely sensed data in the form of images or videos. In this study, we present a prototype model that utilizes multiple remote sensing data for monitoring marine pollution. Our aim is to generate and visualize marine pollution information, which would facilitate swift and precise responses to marine pollution incidents. The automatic production and sharing of Marine Pollution Information (MPI) are crucial steps in effectively addressing on-site oil spills. Of many marine pollutants, oil spills and some floating Hazardous & Noxious Substances (HNS) are the subjects of the work.

A theory of multiaperture along-track interferometric (MA-ATI) synthetic aperture radar (SAR) was previously proposed to estimate the velocity vector of moving targets by utilizing conventional ATI SAR data. In the MA-ATI SAR method, multi/sublook processing is applied to both the raw data acquired by the fore-and-aft antennas, yielding two sets of forward-looking images and two sets of backward-looking images. From the interferograms of corresponding directions, two velocity components can be estimated, and hence the moving directions of a target are also obtained. The velocity vector is calculated by combining the estimated moving direction with the velocity in the range direction measured by the conventional ATI method. In order to show initial validation of this theory, a numerical SAR simulator, which has a function to produce a time series SAR raw data, was further developed to simulate MA-ATI SAR by taking into account the multilook processing. In the present validation study, this simulator (in time-domain processing) was applied to estimate the moving directions of a moving point target and ocean currents in the case of an airborne radar system. In particular, the effect on the subaperture azimuth beam angle was discussed since it is a key factor for the estimation. The preliminary results demonstrated that the estimation accuracy exhibited a direction error of 5°-10° for both the moving target and ocean currents, respectively, when half of the subaperture azimuth beam angle was larger than 1.25°.

Classification of a ship based on satellite imagery usually results differently depending on the type of polarization used in image generation. Also, the different ship's orientation in each image degrades the performance of image-based classification. Given these points, Sentinel-1 data also needs some methods to classify the type of ship. For the classification, we have produced a ship dataset, KIOST-OpenSARShip, which was modified from the OpenSARShip dataset. We compared the brightness of each pixel of ship images generated by different polarizations. Based on this, we created a new image dataset. Then, we increased the similarity between ship images of the same type by aligning the heading direction in the ship images. As a result, our new datasets improve classification performances in some cases compared to using the OpenSARShip. The results of composite images from the VV- and VH-polarized images show up to 19.34% higher accuracy than those using only the one polarized images. In the future, we will improve the performance of the ship classification method considering various characteristics of the ship.

In this letter, a novel theory is proposed to measure the velocity vector of moving targets such as ocean currents by multiaperture along-track interferometric synthetic aperture radar (MA-ATI SAR), utilizing the conventional dual-antenna ATI SAR data. In this system, the range component of the velocity vector is first computed with the conventional ATI SAR algorithm by correlating the two sets of complex images produced through the fore and aft antennas. For estimating the direction of the velocity vector, multilook/sublook processing is applied to two sets of range-compressed azimuth raw data. Now, there are four sets of complex subimages, two of which are forward-looking and the others are backward-looking. Then, from these subimage pairs, the two sublook interferograms of the corresponding directions are produced. The direction of the moving targets can be estimated either from the forward- and backward-looking azimuth velocity components, or directly from the interferometric phases. With this direction and the range velocity component, the velocity vectors of ocean currents and also moving hard targets on land and sea can be estimated.

The present paper describes a theory of estimating velocity vector of ocean currents using the conventional along-track interferometric synthetic aperture radar (ATI SAR or AT-InSAR) data. In the proposed system, termed as multi-aperture along-track interferometric (MA-ATI) SAR, the range velocity vector is computed from the conventional ATI SAR data. For the direction of velocity vector, multi -/sub-look processing in the azimuth direction is applied to each raw data acquired by the fore and aft antennas, yielding four complex sub-images, two of which are forward-looking and the other two are backward-looking. Two interferograms of different azimuth look directions are then computed, and the direction of ocean currents can be measured either from the velocity components of different look directions, or directly from the interferometric phases. With the direction of motion and range velocity computed by the MA- AIT system, the velocity vectors of ocean currents and also moving hard targets on land and sea can be estimated.

This is a short summary of a published work on Synthetic Aperture Radar (SAR)-based land masking for ship detection after applying little modification in the original process [1]. The land masking in SAR images is commonly done by using either archived shoreline databases or an image segmentation which cannot appropriately masks all of the very small islets and exposed sea rocks in the coastal regions of Korean Peninsula. Therefore, in previous work coastline maps from Electronic Navigational Chart (ENC) were just used to retain the land objects from all extracted objects of KOMPSAT-5 images. In this study the method [1] is further improved by applying the ENC-based topography data at various depths which can also mask small sea rocks submerged just beneath the sea surface. Thus, this improved method of land masking could be useful for precise ship detection in SAR images, especially for the geographically complicated coastal regions with many small islets, exposed and submerged sea rocks like Korean Peninsula.

This letter shows the phenomena of Brewster angle damping and its implication observed in the synthetic aperture radar (SAR) images of concrete constructions, such as a bridge and seawalls over the sea. The Fresnel reflection coefficient of concrete material is close to zero at the Brewster angle for X-band V-polarization microwave. The TerraSAR-X images of Tokyo Bay, Japan, at small incidence angles (20.1°-21.4°) showed strong double-bounce reflection between the sea surface and coastal structure with HH-polarization, whereas very little radar backscatter was observed with VV-polarization. The same little radar backscatter was seen in the images of concrete walls on ground and swamp areas covered with reeds. This effect is illustrated with HH/VV intensity and phase difference images, and ground survey data; its implication is also suggested for a better understanding of polarimetric SAR images.

The paper introduces an integrated ship monitoring system, which has been designed and developed with the purpose of real-time maritime surveillance. The integrated system now provides ship information such as position, flag, type etc. over maritime in real-time through the automatic download, processing and visualization steps of data collected from terrestrial-and satellite-based automatic identification systems (AIS) and Sentinel-1. The collected AIS and FMCW radar data are the main source for the real-time remote maritime surveillance, and the SAR-based automatic ship detection system is operated as a SAR mode to integrate the past data because of a time delay in the Sentinel-1 acquisition. The radar and land-based AIS were installed at an ocean tower off the west coast of South Korea. This system will support a rapid and effective surveillance over a huge oceanic area.

The main objective of this research is to investigate the possible use of synthetic aperture radar (SAR) data to monitor sea ice in the southern region of the Sea of Okhotsk. There are a lot of SAR satellites operating in orbit, and most satellites can observe the ground targets with various observation parameters. We would like to find out the suitable observation parameters for monitoring sea ice in relatively thin sea ice area. In-situ data collections on Lake Saroma were carried out in mid February 2012, which were simultaneously with ENVISAT/ASAR and RADARSAT-2 observations. We found that RADARSAT-2 VV to HH co-pol. backscattering ratio decreases as the ice thickness, which was found in our previous work. We will use the measured dielectric constant of the ice surface for improving the backscattering coefficient model for thin sea ice.

This letter describes a technique of extracting and estimating the underwater laver cultivation nets by using the entropy analysis of polarimetric synthetic aperture radar (PolSAR) data. The cultivation nets are placed at 10-20 cm below the sea surface, so that the Bragg waves responsible for L-band radar backscatter do not fully develop in this area of effectively shallow water. Consequently, the surface becomes smooth, and the backscatter radar cross section (RCS) becomes small in comparison with that from deep water without cultivation nets. If RCS from the cultivation area is at the system noise level, the image can be considered as arising from a random process, and the polarimetric entropy should be higher than the open sea area where the radar backscatter is dominated by the single-bounce surface scattering process. We will show that, using the data acquired by Phased Array L-band SAR onboard the Advanced Land Observing Satellite over the Tokyo Bay, Japan, the polarimetric entropy is an effective means of extracting underwater cultivation areas in comparison with the amplitude images. The area of the laver cultivation is then estimated by applying a constant false alarm rate to the entropy images to yield good agreement with the ground-truth data.

The objective of this research is into detecting sea ice by using PALSAR (Phased-Array type L-band SAR) polarimetric data. It is generally difficult to detect thin sea ice area by using the methods based on the backscattering coefficient. We propose a new method utilizing scattering entropy to detect sea ice. This paper shows the results of extracting sea ice area from PALSAR fully polarimetric data acquired from 2009 to 2010. The MODIS (Moderate Resolution Imaging Spectroradiometer) data are used as sea ice reference. The threshold of PALSAR scattering entropy to discriminate sea ice from open water is determined from the distribution of scattering entropy for both sea ice and open water. We compared PALSAR derived sea ice area with MODIS derived area. Since most of PALSAR detected sea ice area is also detected by MODIS data, we can conclude that our proposed method is reliable to detect thin sea ice area in the Sea of Okhotsk. The high resolution backscattering and scattering entropy images give us an idea that there are some difficulties in detecting thin sea ice only by backscattering coefficient.

The communication, ocean and meteorological satellite (COMS) was launched in 26 June 2010. The geostationary ocean color imager (GOCI), one of the three payloads of COMS, will be examined with its data processing system during in-orbit test (IOT). The GOCI data processing system (GDPS) has been verified in code level and function level and system level in each software development phase. GDPS will check the accuracy and performance of the processing by real-time data processing for the generation of reliable ocean color remote sensing data during IOT. By analysis the result, the deeply verification and modification of data processing algorithm and the configuration will be needed. GDPS will be released to public user as basic data processing software of GOCI/COMS.

Since the change in Doppler centroid according to moving targets brings alteration to the phase in azimuth differential signals, one can measure the velocity of the moving targets using this. In this study, we will investigate theoretically measuring velocity of an object from azimuth differential signals by using range compressed data which is the interim outcome of treatment from the simulated Synthetic Aperture Radar (SAR) Raw data of moving targets considering sea clutter. Also, it will provide evaluation for the elements that affect the estimation error of velocity from a single SAR sensor. In the concrete, by making RADARSAT-1 simulated image, the research includes comparisons for the means of velocity measurement classified by directions of movement as in the four following cases. 1. A case in which the object that becomes the target exists independently, 2. When there is a tidal current of 1 m/s, 3. When there exists moving targets of different velocity on the azimuth, 4. When the target is contiguous to the land where it has high back scatter factor. As a result, when the object, which becomes the target, independently exists on SAR image in the range of 128 pixels, the velocity of object could be measured with high accuracy. However, when there existed other moving targets in the range of 128 pixels or when the target was contiguous to the land of high back scatter factor, the velocity was in error by 10% at the maximum. This is because in the process of assuming the target's location, an error occurred due to the disturbed signals affected by the scatterers.

In recent years, the oil spill detection over sea surface and similar oil material filtration are attracting much attention from the ecological point of view, and synthetic aperture radar (SAR) is considered as an effective way of monitoring such phenomenon due to the day-and-night and all weather observation capability. In this paper, results of the oil slick detection experiment by multi-frequency space borne SARs are reported. On December 7, 2007, an oil tanker was wrecked in the Yellow Sea off the Korean west coast, spilling over 12, 000 tons of crude oil, and causing considerable damage on the coastal environment. In order to analyze the impact of the oil spill, we acquired 4 sets of multi-frequency spaceborne SAR images, including TerraSAR-X X-band data, ENVISAT ASAR and RADARSAT-1 C-band data, and ALOS-PALSAR L-band data. We also computed, as a preliminary study, the backscatter radar cross section (RCS) based on the physical optics model at three microwave frequencies for different wave damping ratios by oil slick. In this paper, we describe the present status of the study on oil slick detection, and suggest the possible future direction to be taken.

This paper presents the results of the surface current velocity estimation using 6 Radarsat-1 SAR images and high frequency (HF) radar data acquired in west coastal area near Incheon, Korea. We extracted the surface velocity from SAR images based on the Doppler shift approach in which the azimuth frequency shift is related to the motion of surface target in the radar direction. The extracted SAR current velocities were statistically compared with the current velocities from the HF-radar data. The corrected SAR current velocity inherits the average of HF-radar while maintaining high-resolution mature of the original SAR data.

This study is a preliminary research to extract the oceanic information such as ocean wind and wave using SAR images. The CMOD4, CMOD-IFR2, and polarization ratio (PR) models are used to estimate the magnitude of wind and the SAR-wave spectrum analysis and inter-look cross-spectra methods provides the wave information related to amplitude and direction of the ocean wave over a square-km sized area.

Preliminary results are reported on ship detection using coherence images computed from cross-correlating images of multi-look-processed dual-polarization data of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, cross-correlation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look images.