Related Articles

  • October 31, 2016

    0 39 6

    Detection of Water Bodies from Kompsat-5 SAR Data

    Sang-Eun Park†

    Korean Journal of Remote Sensing 2016; 32(5): 539-550

    Abstract
    Detection of water bodies in land surface is an essential part of disaster monitoring, such as flood, storm surge, and tsunami, and plays an important role in analyzing spatial and temporal variation of water cycle. In this study, a quantitative comparison of different thresholding-based methods for water body detection and their applicability to Kompsat-5 SAR data were presented. In addition, the effect of speckle filtering on the detection result was analyzed. Furthermore, the variations of threshold values by the proportion of the water body area in the whole image were quantitatively evaluated. In order to improve the binary classification performance, a new water body detection algorithm based on the bimodality test and the majority filtering is presented.
  • October 31, 2018

    0 20 4

    Registration Method between High Resolution Optical and SAR Images

    Hyeongju Jeon1) · Yongil Kim2)†

    Korean Journal of Remote Sensing 2018; 34(5): 739-747

    https://doi.org/10.7780/kjrs.2018.34.5.3

    Abstract
    Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.
  • December 31, 2018

    0 8 4

    Reflection Symmetry of PALSAR Quad-Pol Imagery in the Amazon Rainforest

    Jae-Hun Kim1) · Sun Yong Yoon2) · Joong-Sun Won 3)†

    Korean Journal of Remote Sensing 2018; 34(6): 969-979

    https://doi.org/10.7780/kjrs.2018.34.6.1.10

    Abstract
    This paper presents reflection symmetry of polarimetric SAR over the Amazon rainforest in terms of correlation coefficients between the pairs of HH- and HV-pol and VV- and VH-pol data by ALOS PALSAR. The reflection symmetry is defined as a non-zero correlation between HH- and HVpol and VV- and VH-pol over natural distributed targets, and is a fundamental assumption for cross-talk calibration coefficient computation and for three-component decomposition for polarimetric SAR data. The Amazon rainforest is especially one of the common global reference sites for the reflection symmetry. The correlation coefficients for the pairs of reflection symmetry obtained in this study range from 0.018 to 0.097. The results imply that there exists a non-negligible dependency between co-pol and cross-pol in the distributed natural targets, and consequently the non-zero correlation must be considered as a potential contribution to errors of spaceborne SAR polarimetry to some extent.
  • December 31, 2018

    0 49 5

    The Method for Colorizing SAR Images of Kompsat-5 Using Cycle GAN with Multi-scale Discriminators

    Wonhoe Ku 1) · Daewon Chung 2)†

    Korean Journal of Remote Sensing 2018; 34(6): 1415-1425

    https://doi.org/10.7780/kjrs.2018.34.6.3.8

    Abstract
    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.
  • December 31, 2019

    0 19 3
    Abstract
    Existing environmental spatial information, which has been concentrated on spatial resolution, has limitations in solving realistic environmental problems that must be accompanied by physical and chemical characterization. Accordingly, there is a need for an image radar capable of identifying physical characteristics of an object regardless of weather conditions, day and night, and sunlight. Image radar is used in various fields in the United States and Europe. The next generation of medium-sized satellite No. 5 in Korea, which is under development with the aim of monitoring water disasters, is also looking for ways to expand the scope to various applications based on the existing application range. To this end, we analyzed domestic and international papers (100 works) using image radar, and reviewed KEI 2016 report, domestic papers, and foreign papers. Based on this, various environmental issues were summarized and the effects of when the image radar was used were analyzed and land cover was selected as an environmental issue. In the future, we will embody the technology to improve the accuracy of the land cover map, which is the environmental issue selected in this study, and build the foundation system for the stable use of the land cover map.
  • June 30, 2021

    0 54 5
    Abstract
    With the recent increase in available high-resolution (< ~1 m) satellite SAR images, the demand for precise registration of SAR images is increasing in various fields including change detection. The registration between high-resolution SAR images acquired in different look angle is difficult due to speckle noise and geometric distortion caused by the characteristics of SAR images. In this study, registration is performed in two stages, coarse and fine, using the x-band SAR data imaged at staring spotlight mode of TerraSAR-X. For the coarse registration, a method combining the adaptive sampling method and SAR-SIFT (Scale Invariant Feature Transform) is applied, and three rigid methods (NCC: Normalized Cross Correlation, Phase Congruency-NCC, MI: Mutual Information) and one non-rigid (Gefolki: Geoscience extended Flow Optical Flow Lucas-Kanade Iterative), for the fine registration stage, was performed for performance comparison. The results were compared by using RMSE (Root Mean Square Error) and FSIM (Feature Similarity) index, and all rigid models showed poor results in all image combinations. It is confirmed that the rigid models have a large registration error in the rugged terrain area. As a result of applying the Gefolki algorithm, it was confirmed that the RMSE of Gefolki showed the best result as a 1~3 pixels, and the FSIM index also obtained a higher value than 0.02~0.03 compared to other rigid methods. It was confirmed that the mis-registration due to terrain effect could be sufficiently reduced by the Gefolki algorithm.
  • June 30, 2021

    0 58 5

    Surface Change Detection in the March 5 Youth Mine Using Sentinel-1 Interferometric SAR Coherence Imagery

    Jihyun Moon 1)· Geunyoung Kim 2)· Hoonyol Lee 3)†

    Korean Journal of Remote Sensing 2021; 37(3): 531-542

    https://doi.org/10.7780/kjrs.2021.37.3.13

    Abstract
    Open-pit mines require constant monitoring as they can cause surface changes and environmental disturbances. In open-pit mines, there is little vegetation at the mining site and can be monitored using InSAR (Interferometric Synthetic Aperture Radar) coherence imageries. In this study, activities occurring in mine were analyzed by applying the recently developed InSAR coherence-based NDAI (Normalized Difference Activity Index). The March 5 Youth Mine is a North Korean mine whose development has been expanded since 2008. NDAI analysis was performed with InSAR coherence imageries obtained using Sentinel-1 SAR images taken at 12-day intervals in the March 5 Youth Mine. First, the area where the elevation decreased by about 75.24 m and increased by about 9.85 m over the 14 years from 2000 was defined as the mining site and the tailings piles. Then, the NDAI images were used for time series analysis at various time intervals. Over the entire period (2017-2019), average mining activity was relatively active at the center of the mining area. In order to find out more detailed changes in the surface activity of the mine, the time interval was reduced and the activity was observed over a 1-year period. In 2017, we analyzed changes in mining operations before and after artificial earthquakes based on seismic data and NDAI images. After the large-scale blasting that occurred on 30 April 2017, activity was detected west of the mining area. It is estimated that the size of the mining area was enlarged by two blasts on 30 September 2017. The time-averaged NDAI images used to perform detailed timeseries analysis were generated over a period of 1 year and 4 months, and then composited into RGB images. Annual analysis of activity confirmed an active region in the northeast of the mining area in 2018 and found the characteristic activity of the expansion of tailings piles in 2019. Time series analysis using NDAI was able to detect random surface changes in open-pit mines that are difficult to identify with optical images. Especially in areas where in situ data is not available, remote sensing can effectively perform mining activity analysis.
  • October 31, 2021

    0 70 6

    Feasibility Study on FSIM Index to Evaluate SAR Image Co-registration Accuracy

    Sang-Wan Kim 1)† · Dongjun Lee 2)

    Korean Journal of Remote Sensing 2021; 37(5): 847-859

    https://doi.org/10.7780/kjrs.2021.37.5.1.2

    Abstract
    Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR images in change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differences in imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was most suitable for determining the accuracy of image registration. It is likely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.
  • October 31, 2021

    0 64 8

    Detection of Forest Fire Damage from Sentinel-1 SAR Data through the Synergistic Use of Principal Component Analysis and K-means Clustering

    Jaese Lee 1) · Woohyeok Kim 1) · Jungho Im 2)† · Chunguen Kwon 3) · Sungyong Kim 3)

    Korean Journal of Remote Sensing 2021; 37(5): 1373-1387

    https://doi.org/10.7780/kjrs.2021.37.5.3.4

    Abstract
    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.
  • ArticleJune 30, 2022

    0 31 7

    Performance Analysis of Automatic Target Recognition Using Simulated SAR Image

    Sumi Lee1) · Yun-Kyung Lee2) · Sang-Wan Kim3)†

    Korean Journal of Remote Sensing 2022; 38(3): 283-298

    https://doi.org/10.7780/kjrs.2022.38.3.5

    Abstract
    As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.
KSRS
December 2024 Vol. 40, No. 6, pp. 881-1521

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