• Korean Journal of Remote Sensing
• /
• v.34 no.5
• /
• pp.801-810
• /
• 2018

Ship monitoring using satellite synthetic aperture radar (SAR) images consists of ship detection, ship discrimination, and ship classification. A large number of methods have been proposed to improve the detection and discrimination capabilities, while only a few studies exist for ship classification. Thus, many studies for the ship classification are needed to construct ship monitoring system having high performance. Note that constructing database (DB), which contains both SAR images and labels of various ships, is important for research on the ship classification. In the airborne SAR classification, many methods have been developed using moving and stationary target acquisition and recognition (MSTAR) DB. However, there has been no publicly available DB for research on the ship classification using satellite SAR images. Recently, Shanghai Key Laboratory has constructed OpenSARShip DB using both SAR images of various ships generated from Sentinel-1 satellite of European Space Agency (ESA) and automatic identification system (AIS) information. Thus, the applicability of OpenSARShip DB for ship classification should be investigated by using the concepts of airborne SAR classification which have shown high performances. In this study, ship classification using satellite SAR images are conducted by applying the concepts of airborne SAR classification to OpenSARShip DB, and then the applicability of OpenSARShip DB is investigated by analyzing the classification performances.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_4
• /
• pp.1555-1565
• /
• 2018

Detection of subtle ground deformation of volcanoes plays an important role in evaluating the risk and possibility of volcanic eruptions. Ground-fixed observation equipment is difficult to maintain and cost-inefficient. In contrast, satellite remote sensing can regularly monitor at low cost. In this paper, following the study of Chadwick et al. (2006), which applied the interferometric SAR (InSAR) technique to the Sierra Negra volcano, Galapagos. In order to investigate the deformation of the volcano before 2005 eruption, the recent activities of this volcano were analyzed using Sentinel-1, the latest SAR satellite. We obtained the descending mode Sentinel-1A SAR data from January 2017 to January 2018, applied the Persistent Scatter InSAR, and estimated the depth and expansion quantity of magma in recent years through the Mogi model. As a result, it was confirmed that the activity pattern of volcano prior to the eruption in June 2018 was similar to the pattern before the eruption in 2005 and was successful in estimating the depth and expansion amount. The results of this study suggest that satellite SAR can characterize the activity patterns of volcano and can be possibly used for early monitoring of volcanic eruption.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.2
• /
• pp.83-92
• /
• 2018

Using ground moving target indicators equipped with synthetic aperture radars for locating moving targets within a wide background clutter in a short time is an excellent method for monitoring traffic. Although the displaced phase center antenna (DPCA) technique and along track interferometry (ATI) are real time methods with low computational complexity, they are essential for reducing cases of false alarm that can result in poor performance. In this paper, we propose two detection methods using DPCA and ATI-the parallel fusion method and serial fusion method. Simulation results demonstrate that the proposed detection methods are characterized by low probability of false alarm along with good performance. In particular, the serial fusion method possesses high detection probability along with low probability of false alarm (1/5th of the false alarm probability of the DPCA technique).

• Journal of Cadastre & Land InformatiX
• /
• v.47 no.2
• /
• pp.79-90
• /
• 2017

Urban environment represent one of the most dynamic regions on earth. As in other countries, forests, green areas, agricultural lands are rapidly changing into residential or industrial areas in South Korea. Monitoring such rapid changes in land use requires rapid data acquisition, and satellite imagery can be an effective method to this demand. In general, SAR(Synthetic Aperture Radar) satellites acquire images with an active system, so the brightness of the image is determined by the surface roughness. Therefore, the water areas appears dark due to low reflection intensity, In the residential area where the artificial structures are distributed, the brightness value is higher than other areas due to the strong reflection intensity. If we use these characteristics of SAR images, settlement areas can be extracted efficiently. In this study, extraction of settlement areas was performed using TerraSAR-X of German high-resolution X-band SAR satellite and KOMPSAT-5 of South Korea, and object-oriented image classification method using the image segmentation technique is applied for extraction. In addition, to improve the accuracy of image segmentation, the speckle divergence was first calculated to adjust the reflection intensity of settlement areas. In order to evaluate the accuracy of the two satellite images, settlement areas are classified by applying a pixel-based K-means image classification method. As a result, in the case of TerraSAR-X, the accuracy of the object-oriented image classification technique was 88.5%, that of the pixel-based image classification was 75.9%, and that of KOMPSAT-5 was 87.3% and 74.4%, respectively.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1373-1387
• /
• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Korean Journal of Remote Sensing
• /
• v.26 no.2
• /
• pp.123-131
• /
• 2010

In this paper we applied Ground-Based Synthetic Aperture Radar(GB-SAR) interferometry to detect artificial displacement of a reflector and performed an atmospheric humidity correction to improve the accuracy. A series of GB-SAR images were obtained using a center frequency of 5.3 GHz with a range resolution of 25 cm and a azimuth resolution of $0.324^{\circ}$, all in full-polarization (HH, VV, VH, HV) modes. A triangular trihedral corner reflector was located 160 m away from the system, and the artificial displacements of 0-40 mm was implemented during the GB-SAR image acquisition. The result showed that the RMS error between the actual and measured displacements, averaged in all polarization data, was 1.22 mm, while the maximum error in case of the 40 mm displacement was 2.72 mm at HH-polarization. After the atmospheric correction with respect to the humidity, the RMS error was reduced to 0.52 mm. We conclude that a GB-SAR system can be used to monitor the possible displacement of artificial/natural scatterers and the stability assessment with sub-millimeter accuracy.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1341-1352
• /
• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• The Journal of Engineering Geology
• /
• v.34 no.1
• /
• pp.39-49
• /
• 2024

The construction of underground infrastructure is garnering growing increasing research attention owing to population concentration and infrastructure overcrowding in urban areas. An important associated task is establishing a monitoring system to evaluate stability during infrastructure construction and operation, which relies on developing techniques for ground investigation that can evaluate ground stability, verify design validity, predict risk, facilitate safe operation management, and reduce construction costs. The method proposed here uses satellite imaging in a cost-effective and accurate ground investigation technique that can be applied over a wide area during the construction and operation of infrastructure. In this study, analysis was performed using Synthetic Aperture Radar (SAR) data with the time-series radar interferometric technique to observe surface displacement during the construction of urban underground roads. As a result, it was confirmed that continuous surface displacement was occurring at some locations. In the future, comparing and analyzing on-site measurement data with the points of interest would aid in confirming whether displacement occurs due to tunnel excavation and assist in estimating the extent of excavation impact zones.

• Bulletin of the Korean Space Science Society
• /
• 2009.10a
• /
• pp.46.3-46.3
• /
• 2009

The SAR (Synthetic Aperture Radar) satellite has the advantage of implementing the imaging mission even though it is night time, cloudy weather, and all weather conditions, which is different from the satellite with the optical payload. This is the reason why the SAR satellite comes into the spotlight in the observation satellite field. The Korea Aerospace Research Institute (KARI) has been developing the first Korean SAR satellite and is currently integrating and testing the Flight Model. For the launch vehicle service, KARI finalized the selection of the launch vehicle service provider and finished Critical Design Review (CDR) of the interface between the bus and the launch vehicle. KARI and launch vehicle service provider also finished the test of the telemetry interface between the bus and the launch vehicle. The test of the telemetry interface has the purpose of checking the interface of the telemetry which is the SOH(State-of-Health) of the satellite in an early launch stage. For this test, KARI has finished the development of the spacecraft simulator which is composed of the bus simulator to generate the analog telemetry and the launch vehicle simulator to gather the telemetry. In this research, the result of the hardware implementation and the software implementation for the spacecraft simulator were described. Finally the results of the launch vehicle telemetry MUX test which were performed at the launch vehicle provider's design office by using the spacecraft simulator were summarized. It is expected that this simulator will be used in the next test after the manufacture of the launch vehicle.

• The Journal of Engineering Geology
• /
• v.32 no.3
• /
• pp.363-376
• /
• 2022

An area under construction for a living facility collapsed around 12:48 KST on 13 January 2021 in Sa-dong, Ansan-si, Gyeonggi-do. There were no casualties due to the rapid evacuation measure, but part of the temporary retaining facility collapsed, and several cracks occurred in the adjacent road on the south side. This study used the potential of synthetic aperture radar (SAR) satellite for surface property changes that lies in backscattering characteristic to map the collapsed structure. The interferometric SAR technique can make a direct measurement of the decorrelation among different acquisition dates by integrating both amplitude and phase information. The damage proxy map (DPM) technique has been employed using four high-resolution Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) data spanning from 2020 to 2021 during ascending observation to analyze the collapse of the construction. DPM relies on the difference of pre- and co-event interferometric coherences to depict anomalous changes that indicate collapsed structure in the study area. The DPMs were displayed in a color scale that indicates an increasingly more significant ground surface change in the area covered by the pixels, depicting the collapsed structure. Therefore, the DPM technique with SAR data can be used for damage assessment with accurate and comprehensive detection after an event. In addition, we classify the amplitude information using support vector machine (SVM) and maximum likelihood classification algorithms. An investigation committee was formed to determine the cause of the collapse of the retaining wall and to suggest technical and institutional measures and alternatives to prevent similar incidents from reoccurring. The report from the committee revealed that the incident was caused by a combination of factors that were not carried out properly.