• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.447-463
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• 2019

Sea surface wind is a fundamental element for understanding the oceanic phenomena and for analyzing changes of the Earth environment caused by global warming. Global research institutes have developed and operated scatterometers to accurately and continuously observe the sea surface wind, with the accuracy of approximately ${\pm}20^{\circ}$ for wind direction and ${\pm}2m\;s^{-1}$ for wind speed. Given that the spatial resolution of the scatterometer is 12.5-25.0 km, the applicability of the data to the coastal area is limited due to complicated coastal lines and many islands around the Korean Peninsula. In contrast, Synthetic Aperture Radar (SAR), one of microwave sensors, is an all-weather instrument, which enables us to retrieve sea surface wind with high resolution (<1 km) and compensate the sparse resolution of the scatterometer. In this study, we investigated the Geophysical Model Functions (GMF), which are the algorithms for retrieval of sea surface wind speed from the SAR data depending on each band such as C-, L-, or X-band radar. We reviewed in the simulation of the backscattering coefficients for relative wind direction, incidence angle, and wind speed by applying LMOD, CMOD, and XMOD model functions, and analyzed the characteristics of each GMF. We investigated previous studies about the validation of wind speed from the SAR data using these GMFs. The accuracy of sea surface wind from SAR data changed with respect to observation mode, GMF type, reference data for validation, preprocessing method, and the method for calculation of relative wind direction. It is expected that this study contributes to the potential users of SAR images who retrieve wind speeds from SAR data at the coastal region around the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.447-460
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• 2022

It is well known that satellite synthetic aperture radar interferometry (InSAR) has been widely used for the observation of surface displacement owing to earthquakes, volcanoes, and subsidence very precisely. In wetlands where vegetation exists on the surface of the water, it is possible to create a water level change map with high spatial resolution over a wide area using the InSAR technique. Currently, a number of imaging radar satellites are in operation, and most of them support a ScanSAR mode observation to gather information over a large area at once. The Cienaga Grande de Santa Marta (CGSM) wetland, located in northern Colombia, is a vast wetland developed along the Caribbean coast. The CGSM wetlands face serious environmental threats from human activities such as reclamation for agricultural uses and residential purposes as well as natural causes such as sea level rise owing to climate change. Various restoration and protection plans have been conducted to conserve these invaluable environments in recognition of the ecological importance of the CGSM wetlands. Monitoring of water level changes in wetland is very important resources to understand the hydrologic characteristics and the in-situ water level gauge stations are usually utilized to measure the water level. Although it can provide very good temporal resolution of water level information, it is limited to fully understand flow pattern owing to its very coarse spatial resolution. In this study, we evaluate the L-band ALOS-2 PALSAR-2 ScanSAR mode to observe the water level change over the wide wetland area using the radar interferometric technique. In order to assess the quality of the interferometric product in the aspect of spatial resolution and coherence, we also utilized ALOS-2 PALSAR-2 stripmap high-resolution mode observations.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.1-4
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• 2008

근년 일본, 캐나다, 호주, 미국, EU(주로 노르웨이, 영국) 등에서 인공위성을 이용한 해양 안전의 확보를 위한 연구개발이 진행되고 있으며, 일부 실해역 적용의 분야도 도출되고 있는 실정이다. 9.11테러 이후, 국제해사기구에서도 해상보안의 문제는 주요 이슈로 대두되어, 해상보안에의 활용 기술 개발이 먼저 시작되었다. 그 외, 밀입국 선박 감시 덴 해양오염 모니터링이 주요 활용분야이다. 간단하게 요약하면 다음과 같다. -노르웨이: Norwegian Defence Hesearch Establishment(NDRE)에서 주도적으로 선박 탐지 실험 및 기술 개발을 실시. 주로, ESA의 위성을 활용. 국가 보안의 목적으로는 적용을 하고 있음. -캐나다: 캐나다에서 소유하고 있는 RADARSAT을 이용하여 가장 많은 실험을 실시함. 영상을 처리하고 결과에 대한 평가를 수행하기 위한 시스템(Ocean Monitoring Workstation, OSM)을 개발하여 보급에 주력. -호주: 주로 캐나다의 위성 및 시스템의 적용을 하고 있음 영해 및 환경 감시의 역할을 수행. Coastwatch조직을 만들어 해상 감시활동을 하고 있음. -영국: 데이터 취득 후, 2.5시간 이내에 선박의 위치를 전송하는 인터페이스를 개발함. 일본의 경우, 다른 선진국에 비해서는 다소 늦게 시작되었다. 2003년 발간된 '재해 등에 대응한 인공위성이용기술에 관한 종합보고서'를 시작으로 정보수집위성 4기 및 지구관측위성을 이용한 해양 감시 활동이 시작되었다. 또한, 제 3기 과학기술기본계획(2006-2012)내에 해양 불법침입 탐지 기술 개발 항목이 반영되어 있다. 유럽의 해상보안서비스(MARISS)의 사용자 워크숍이 ESA ESRIN(이탈리아 프라스카티)에서 2008년 1월 22일 열렸다. 실질적인 내용은, '해상보안을 위한 우주 시스템'에 관한 것으로 인공위성 이용하는데 있어 설계안 및 데이터 이용 컨셉을 제시하는 것이었다. 여기서 중요한 것은 국가간의 협력이 절대적으로 필요하며, 기존의 시스템과의 통합에 있어 신뢰성을 어떻게 확보하는가에 있다고 할 수 있다. 또한, 보안과 환경모니터링의 기능이 분리되어 진행되고 있는 부분에 대한 정보 통합 방향도 제기되었다. 국내에서도 AIS와 SAR정보의 결합에 관한 검토는 이루어졌으며, 이를 바탕으로 EU와 같은 시스템의 구축(조직과 연구개발)을 위한 실질적인 검토가 필요하다.

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