• Journal of the Korean Society of Marine Environment & Safety
• /
• v.10 no.1 s.20
• /
• pp.1-7
• /
• 2004

RADARSAT is one of many possible data sources that can play an important role in marine surveillance including ship detection because radar sensors have the two primary advantages: all-weather and day or night imaging. However, atmospheric effects on SAR imaging can not be bypassed and any remote sensing image has various geometric distortions, In this study, radiometric and geometric calibrations for RADARSAT/SAT data are tried using SGX products georeferenced as level 1. Even comparison of the near vs. far range sections of the same images requires such calibration Radiometric calibration is performed by compensating for effects of local illuminated area and incidence angle on the local backscatter, Conversion method of the pixel DNs to beta nought and sigma nought is also investigated. Finally, automatic geometric calibration based on the 4 pixels from the header file is compared to a marine chart. The errors for latitude and longitude directions are 300m and 260m, respectively. It can be concluded that the error extent is acceptable for an application to open sea and can be calibrated using a ground control point.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.28 no.8
• /
• pp.636-645
• /
• 2017

A scanning radar is exploited widely such as for ground surveillance, disaster rescue, and etc. However, the range resolution is limited by transmitted bandwidth and cross-range resolution is limited by beam width. In this paper, we propose a method for super-resolution radar imaging. If the distribution of reflectivity is sparse, the distribution is called sparse signal. That is, the problem could be formulated as compressive sensing problem. In this paper, 2D super-resolution radar image is generated via reweighted ${\ell}_1-Minimization$. In the simulation results, we compared the images obtained by the proposed method with those of the conventional Orthogonal Matching Pursuit(OMP) and Synthetic Aperture Radar(SAR).

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.439-439
• /
• 2022

인공위성은 위성통신, 기상 등 다양한 분야에서 활용되고 있지만 재난과 위성영상 특성 매칭의 제약으로 재난 상황에서는 제한적으로 사용되었다. 국내외 위성 갯수의 증가로 위성영상을 준-실시간으로 확보 가능함에 따라 활용할 수 있는 범위가 증가하여 최근에는 재난·재해에 신속하게 대비하기 위한 연구가 활발히 진행되고 있다. 본 연구는 재난 발생 지역의 위성 영상 확보를 위해 촬영된 영상과 미래시점의 촬영 예정인 영상의 촬영 예정 시간 및 영역을 빠른 시간 내 분석하여 최적 위성영상 확보에 기반이 되고자 한다. 행정안전부에서 분류한 재난·재해 유형에 따라 재난 예측, 탐지, 사후처리를 위한 위성자료의 확보를 위하여 다양한 위성과 탑재된 센서들의 궤도, 공간 해상도, 파장대 등의 위성영상의 적시성을 분석하여 최적 위성을 정의하였다. 위성 궤도 시뮬레이션은 TLE(Two Line Element) 정보를 이용하는 SGP4(Simplified General Perturbations version 4) 모델에 적용하여 개발하였다. 최신 TLE 정보를 이용하여 위성 궤도 정보 및 센서 정보(공간 해상도, Swath width, incidence angle IFOV 등)을 적용하였다. 수집된 위성 궤도 정보를 기반으로 위성의 궤도를 예측하여 예측된 위치에서의 촬영 영역을 산정하는 분석 기능을 수행하여 최종 시뮬레이션 데이터를 생성한다. 개발된 위성 궤도 시뮬레이션 알고리즘을 토대로 태풍 미탁 사례에 적용하였다. 위성 궤도 시뮬레이션 알고리즘을 태풍 미탁 사례에 적용한 결과 다종 위성리스트 중 위성 궤도 분석을 통해 최단기간 획득 가능한 위성 중 정지 궤도 기상위성인 Himawari-8, GK-2A는 태풍 경로 모니터링, 광학 위성인 Sentinel-2, PlanetScope는 건물 피해 지역, SAR 위성인 Sentinel-1, ICEYE는 홍수 지역을 탐지하는데 최적 위성 영상으로 분석되었다.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_4
• /
• pp.1479-1488
• /
• 2018

On September 3rd 2017, strong artificial seismic signals from North Korea were detected in KMA (Korea Meteorological Administration) seismic network. The location of the epicenter was estimated to be Punggye-ri nuclear test site and it was the most powerful to date. The event was not studied well due to accessibility and geodetic measurements. Therefore, we used remote sensing data to analyze surface changes around Mt. Mantap area. First of all, we tried to detect surface deformation using InSAR method with Advanced Land Observation Satellite-2 (ALOS-2). Even though ALOS-2 data used L-band long wavelength, it was not working well for this particular case because of decorrelation on interferogram. The main reason would be large deformation near the Mt. Mantap area. To overcome this limitation of decorrelation, we applied offset tracking method to measure deformation. However, this method is affected by window kernel size. So we applied various window sizes from 32 to 224 in 16 steps. We could retrieve 2D surface deformation of about 3 m in maximum in the west side of Mt. Mantap. Second, we used Pleiadas-A/B high resolution satellite optical images which were acquired before and after the 6th nuclear test. We detected widespread surface damage around the top of Mt. Mantap such as landslide and suspected collapse area. This phenomenon may be caused by a very strong underground nuclear explosion test. High-resolution satellite images could be used to analyze non-accessible area.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_3
• /
• pp.1095-1107
• /
• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.65-65
• /
• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Korean Journal of Remote Sensing
• /
• v.24 no.2
• /
• pp.165-177
• /
• 2008

Small baseline subset (SBAS) algorithm has been recently developed using an appropriate combination of differential interferograms, which are characterized by a small baseline in order to minimize the spatial decorrelation. This algorithm uses the singular value decomposition (SVD) to measure the time-series surface deformation from the differential interferograms which are not temporally connected. And it mitigates the atmospheric effect in the time-series surface deformation by using spatially low-pass and temporally high-pass filter. Nevertheless, it is not easy to correct the phase unwrapping error of each interferogram and to mitigate the time-varying noise component of the surface deformation from this algorithm due to the assumption of the linear surface deformation in the beginning of the observation. In this paper, we present an improved SBAS technique to complement these problems. Our improved SBAS algorithm uses an iterative approach to minimize the phase unwrapping error of each differential interferogram. This algorithm also uses finite difference method to suppress the time-varying noise component of the surface deformation. We tested our improved SBAS algorithm and evaluated its performance using 26 images of ERS-1/2 data and 21 images of RADARSAT-1 fine beam (F5) data at each different locations. Maximum deformation amount of 40cm in the radar line of sight (LOS) was estimated from ERS-l/2 datasets during about 13 years, whereas 3 cm deformation was estimated from RADARSAT-1 ones during about two years.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1475-1490
• /
• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• The KIPS Transactions:PartB
• /
• v.13B no.4 s.107
• /
• pp.369-376
• /
• 2006

The degradation and its inverse modeling can achieve restoration of corrupted image, caused by scaled digitization and electronic transmission. De-speckle process on the noisy document(or SAR) images is one of the basic examples. Non-linearity of the speckle noise model may hinder the inverse process. In this paper, our study is focused on investigation of the restoration methods for bi-level document image degraded by the impulse noise model. Our study shows that, on bi-level document images, the weighted-median filter and the Lee filter methods are very effective among other spatial filtering methods, but wavelet filter method is ineffective in aspect of processing speed: approximately 100 times slower. Optimal values of the weight to be used in the weighted median filter are investigated and presented in this paper.

• Ocean Systems Engineering
• /
• v.13 no.2
• /
• pp.195-224
• /
• 2023

This paper proposes a method for the acoustic imaging wherein the traditional requirement of the relative movement between the transmitter and target is overcome. This is facilitated through the beamforming acoustic array in the transmitter, in which the target is illuminated by the array at various azimuth and elevation angles without the physical movement of the acoustic array. The concept of beam steering of the acoustic array facilitates the formation of the beam at desired angular positions of azimuth and elevation angles. This paper substantiates that the combination of illumination of the target from different azimuth and elevation angles with respect to the transmitter (through the beam steering of beam forming acoustic array) and the beam steering at multiple frequencies (through SF) results in enhanced reconstruction of images of the target in the underwater scenario. This paper also demonstrates the possibility of reconstruction of the image of a target in underwater without invoking the traditional algorithms of Digital Image Processing (DIP). This paper comprehensively and succinctly presents all the empirical formulae required for modelling the acoustic medium and the target to facilitate the reader with a comprehensive summary document incorporating the various parameters of multi-disciplinary nature.