• Journal of electromagnetic engineering and science
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• v.14 no.4
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• pp.405-410
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• 2014

In this paper, we compare the quality of images reconstructed by a conventional delayed-sum (DS) algorithm and radiation pattern-based DS algorithm. In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The radiation pattern-based DS algorithm enhances the TCR of the image by focusing the target signals and preventing contamination of the radar scene. We first consider synthetic data obtained through GprMax2D/3D, a finite-difference time-domain (FDTD) forward solver. Experimental data of a 2-GHz bandwidth stepped-frequency signal are collected using a vector network analyzer (VNA) in an anechoic chamber setup. The radiation pattern-based DS algorithm shows a 6.7-dB higher TCR compared to the conventional DS algorithm.

• Journal of IKEEE
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• v.25 no.1
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• pp.76-82
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• 2021

In this paper, the compressed sensing basic pursuit denoise algorithm adopted to synthetic aperture radar imaging is investigated to improve the object recognition. From the incomplete data sets for image processing, the compressed sensing algorithm had been integrated to recover the data before the conventional back- projection algorithm was involved to obtain the synthetic aperture radar images. This method can lead to the reduction of measurement events while scanning the objects. An ultra-wideband radar scheme using a stripmap synthetic aperture radar algorithm was utilized to detect objects hidden behind the box. The Ultra-Wideband radar system with 3.1~4.8 GHz broadband and UWB antenna were implemented to transmit and receive signal data of two conductive cylinders located inside the paper box. The results confirmed that the images can be reconstructed by using a 30% randomly selected dataset without noticeable distortion compared to the images generated by full data using the conventional back-projection algorithm.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.515-530
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• 2021

This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R²) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R² was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R² was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R² of 0.69 and RMSE of 2.88%.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Proceedings of the KSRS Conference
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• v.2
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• pp.820-823
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• 2006

Nonlinear internal waves (NLIW) were studied as a unusual phenomena in the ocean decades ago. As the quality, quantity and variety of satellite images improve over decades, it is founded that NLIW is a ubiquitous phenomenon. Over the continental shelf of northern South China Sea (SCS), both optical and microwave images show that there are trains of NLIW packets near Dongsha Atoll (20.7N, 116.8E). Each packet contains several NLIW fronts. These NLIW packets are nearly parallel to each other and they are refracted, reflected or diffracted by the change of ocean bottom topography. Based on Korteweg de Vries (KdV) theory and the assumption that the bright/dark lines in the satellite images are centers of convergence/divergence of NLIW fronts, one may (1) sort NLIW packets in the same satellite image into groups of the same source, but generated at different tidal cycles, (2) relate NLIW packets in consecutive satellite images of one day apart, (3) locating faint signals of NLIW fronts in a satellite image. The NLIWs travel more than 100 km/day near Dongsha Atoll, with higher speed in deeper water. The bias and standard deviation of predicted location of NLIW front from its true location is about 1% and 5.1%, respectively.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.275-288
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• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.545-552
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• 2018

Grounding line is used as evidence of the mass balance showing the vulnerability of Antarctic glaciers and ice shelves. In this research, we utilized a high resolution digital elevation model of glacier surface derived by recently launched satellites to estimate the position of grounding line of Campbell Glacier in East Antarctica. TanDEM-X and TerraSAR-X data in single-pass interferometry mode were acquired on June 21, 2013 and September 10, 2016 and CryoSat-2 radar altimeter data were acquired within 15 days from the acquisition date of TanDEM-X. The datasets were combined to generate a high resolution digital elevation model which was used to estimate the grounding line position. During the 3 years of observation, there weren't any significant changes in grounding line position. Since the average density of ice used in estimating grounding line is not accurately known, the variations of the grounding line was analyzed with respect to the density of ice. There was a spatial difference from the grounding line estimated by DDInSAR whereas the estimated grounding line using the characteristics of the surface of the optical satellite images agreed well when the ice column density was about $880kg/m^3$. Although the reliability of the results depends on the vertical accuracy of the bathymetry in this study, the hydrostatic ice thickness has greater influence on the grounding line estimation.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.104-113
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• 2012

Followed by that HTML5(Hypertext Markup Language5) was introduced, many kinds of program and services based on this have been developed and released. HTML5 is technical standard specifications for cross platform for personal computers and mobile devices so that it is expected that continuing progress and wide application in the both sides of the academic and the industrial fields increase. This study is to design and implement a mobile application program for overlay rendering with DEM and other geo-based image sets using HTML5 WebGL for 3D graphic processing on web environment. Particularly, the blending technique was used for overlay processing with multiple images. Among available WebGL frameworks, CubicVR.js was adopted, and various blending techniques were provided in the user interface for general users. For the actual application in the study area around the Sejong city, serveral types of geo-based data sets were used and processed: KOMPSAT-2 images, ALOS PALSAR SAR images, and grid data by environment measurements. While, DEM for 3D viewing with these geo-based images was produced using contour information of the digital map sets. This work demonstrates possibilities that new types of contents and service system using geo-based images can be extracted and applied.