• Journal of Ocean Engineering and Technology
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• v.27 no.6
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• pp.100-105
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• 2013

This paper presents a sea surface reconstruction method that uses measured radar images by applying filtering techniques and identifying wave characteristics of the surrounding the Ieodo ocean research station using WaveFinder (X-band wave measurement radar), which is installed in the station. In addition, the results obtained from real radar images are used to verify the reconstructed sea surface. WaveFinder is a marine system that was developed to measure wave information in real time. The WaveFinder installed in the station could acquire sequent images for the sea surface at constant time intervals to obtain real time information (Wave height, mean wave period, wave directionality, etc.) for the wave by getting a three-dimensional spectrum by applying an FFT algorithm to the acquired sequent images and wave dispersion relation. In particular, we found the wave height using the SNR (Signal to noise ratio) of the acquired images. The wave information measured by WaveFinder could be verified by comparing and analyzing the results measured using the wave measurement instrument (Sea level monitor) in the station. Additionally, the wave field around the station could be reconstructed through the three-dimensional spectrum and the inverse FFT filtering from the analyzed results for the measured radar images. We verified the applicability of the sea surface reconstruction method by comparing the measured and simulated sea surfaces.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.26.3-27
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• 2009

한 기의 영상레이더 위성을 이용하여 동일한 촬영지역에 대해 적절한 기선벡터(Baseline)을 유지하는 두 장(scene)의 영상을 획득하여 그 지역의 정밀 표고차를 추출하는 레이더 간섭계(Interferometry) 임무를 수행하기 위해서는 반복지상궤적을 유지하도록 위성의 궤도를 주기적으로 조정해 주어야 한다. 이 연구에서는 반복지상궤적 유지 정밀도를 극대화시키기 위하여 최적의 기준궤도를 생성하고 이를 유지하기 위한 속도증분 및 궤도 조정 일정을 산출할 수 있는 궤도최적화 S/W 를 개발하였다. 이 연구의 최적 궤도 설계 문제는 다음과 같다. "시작시간 $T_0$에서 초기 접촉궤도 상태벡터 (ECEF 위치 및 속도벡터) $x_0$이고, 지상궤적반복주기 p 이후의 시간 $T_0+p$에서도 초기 접촉궤도 상태벡터와 동일한$x_0$가 되도록 궤도를 유지하려고 할 때, 여명 궤도(dawn-dusk and sun-synchronous orbit)에서 운영되는 위성의 연료소모(또는 속도증분)를 최소화시키는 가상의 궤도조정(maneuver) 횟수, 시기, 크기를 찾아라." 이 연구에서는 궤도최적화 문제를 풀기 위하여 GRACE 중력모델(GGM02C)이 적용된 수치적 방법의 위성궤도예측 알고리즘을 시스템 설계에 적용하였고, 매개변수 최적화 방법 중 구속조건이 있는 비선형 최적화 기법의 하나인 연속 2차 계획법(sequential quadratic programming)을 사용하여 해를 구하였다. 개발된 궤도최적화 S/W의 성능을 분석하기 위하여 고도 550km의 여명궤도를 돌며 지상궤적반복주기가 28일인 영상레이더 위성에 대해 적용하였다. 해석 결과를 통해, 비록 시스템의 비선형이 큼에도 불구하고 최소의 속도증분으로 정밀한 반복지상궤적이 유지됨을 알 수 있었다.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1271-1280
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• 2012

In the recent parking assistant systems, finding out the distance to the object behind a car is often performed by the range sensors such as ultrasonic sensors, radars. However, the installation of additional sensors on the used vehicle could be difficult and require extra cost. On the other hand, the motion stereo technique that extracts distance information using only an image sensor was also proposed. However, In the stereo rectification step, the motion stereo requires good features and exacts matching result. In this paper, we propose a fast algorithm that extracts the accurate distance information for the parallel parking situation using the consecutive images that is acquired by a rear-view camera. The proposed algorithm uses the quadrangle transform of the image, the horizontal line integral projection, and the blocking-based correlation measurement. In the experiment with the magna parallel test sequence, the result shows that the line-accurate distance measurement with the image sequence from the rear-view camera is possible.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.215-224
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• 2013

This paper proposes a robust lane detection algorithm for bumpy or slope changing roads by estimating extrinsic camera parameters, which represent the pose of the camera mounted on the car. The proposed algorithm assumes that two lanes are parallel with the predefined width. The lane detection and the extrinsic camera parameter estimation are performed simultaneously by utilizing B-snake in motion compensated and merged feature map with consecutive sequences. The experimental results show the robustness of the proposed algorithm in various road environments. Furthermore, the accuracy of extrinsic camera parameter estimation is evaluated by calculating the distance to a preceding car with the estimated parameters and comparing to the radar-measured distance.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1489-1501
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• 2018

In 2016 Kumamoto, Japan, the foreshocks of $M_j$ 6.5 and 6.4, mainshock of $M_j$ 7.3 besides more than 2,000 aftershocks occurred in succession. Large surface deformation occurred due to this serial earthquakes and three-dimensional measurements of the deformation have been presented for the study of fault structures (Baek, 2017). The 3d measurements retrieved from two ascending pairs (20160211_20160602, 20151119_20160616) and a descending pair (20160307_20160418) acquired from ALOS PALSAR-2. In order to avoid mixing ionospheric error components on along-track surface deformation, the descending multiple-aperture interferogram, which do not contain the deformation of aftershocks after 20160418, was utilized. For these reason, there was a temporal discrepancy of about 2 months in extracting the north-south deformation. In this study, we applied a directional filter based ionospheric correction to ascending multiple-aperture interferograms, in order to reduce this discrepancy and understand more accurate fault movements. As a result of the ionospheric correction, an additional displacement signal was observed nearby fault lines. The root-mean-squared errors compared to GPS were about 9.87, 8.13 cm respectively. These results show improvements of 4.8 and 6.4 times after ionospheric correction. We expected that these along-track measurements would be used to decide more accurate movements of faults related to the 2016 Kumamoto Earthquake.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• 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.