• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.395-408
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• 2015

High-resolution satellite image mosaics are becoming increasingly important in the field of remote sensing image analysis as an essential image processing to create a large image constructed from several smaller images. In this paper, we present an automatic seamline extraction technique and the procedure to generate a mosaic image by this technique. For more effective seamline extraction in the overlap region of adjacent images, an NDVI-based seamline extraction technique is developed, which takes advantage of the computational time and memory. The Normalized Difference Vegetation Index(NDVI) is an index of plant "greeness" or photosynthetic activity that is employed to extract the initial seamline. The NDVI can divide into manmade region and natural region. The cost image is obtained by the canny edge detector and the buffering technique is used to extract the ranging cost image. The seamline is extracted by applying the Dijkstra algorithm to a cost image generated through the labeling process of the extracted edge information. Histogram matching is also conducted to alleviate radiometric distortion between adjacent images acquired at different time. In the experimental results using the KOMPSAT-2/3 satellite imagery, it is confirmed that the proposed method greatly reduces the visual discontinuity caused by geometric difference of adjacent images and the computation time.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4418-4437
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• 2017

In this paper a three-phase secure compressive sensing (CS) and received signal strength (RSS) based target localization approach is proposed to mitigate the effect of malicious node attack. RSS measurements are first arranged into a group of subsets where the same measurement can be included in multiple subsets. Intermediate target position estimates are then produced using individual subsets of RSS measurements and the CS technique. From the intermediate position estimates, the residual error vector and residual error square vector are formed. The least median of residual error square is utilized to define a verifier parameter. The selected residual error vector is utilized along with a threshold to determine whether a node or measurement is under attack. The final target positions are estimated by using only the attack-free measurements and the CS technique. Further, theoretical analysis is performed for parameter selection and computational complexity evaluation. Extensive simulation studies are carried out to demonstrate the advantage of the proposed CS-based secure localization approach over the existing algorithms.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.104-110
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• 2001

Ocean heat fluxes over a wide region are generally estimated by an aerodynamic bulk fromula. Though a remote sensing technique can be expected to estimated global heat flux, it is difficult to obtain air temperature and specific humidity at sea surface by a remote sensor. In this study present a new method with which to determine near-sea surface air temperature from in situ data. Also, These methods compared with other methods. A new method used a linear regression equation between sea surface temperature and air temperature of the buoys data. In this study new method is validated using observed monthly mean data at the Japan Meteorological Agency(JMA), National Data Buoy Center(NDBC) and Tropical Ocean-Global Atmosphere(TOGA)-Tropical Atmosphere Ocean(TAO) buoys. The result that bias and rmse are 0.28, 1.5$0^{\circ}C$ respectively. The correlation coefficient is 0.98. Also, to retrieve near-sea surface specific humidity(Q) from good nonlinear regression relationship between vapor pressure(Ea) of buoy data and air temperature, after obtained the third-order polynomial function, compared with that of estimated from SSM/I empirical equation by Schussel et al(1995). The result that bias and rmse are -1.42 and 1.75(g/kg).

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.3
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• pp.3-12
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• 2009

Soil is one of the Earth's most important resources. There are many differences among the soils of plains.like and hilly terrains, and therefore, accurate and comprehensive information on soil is essential for optimum and sustainable soil utilization. However, information on the soil of the hilly terrains of the Eastern Ghats of Tamil Nadu, India, is limited or absent. In the present study, Kolli hill, one among the hills of the Eastern Ghats, was soil.inventoried and mapped using a ground survey and remote sensing. Soil samples were collected and their physico.chemical properties analyzed according to the United States Department of Agriculture (USDA) standards. The soils were classified up to the family level. As a result of this study, 30 soil series belonging to ten sub.groups of five great groups and three sub.orders and orders each, were identified (classified to the family level) and mapped. Entisols, Inseptisols and Alfisols were the three orders, among which Entisols was the major one, occupying 75% of the area. Among the five great groups, Ustorthents occupied majority of the area (73%). Lithic Ustorthents and Typic Ustorthents were the two major sub.groups, occupying 40% and 26% of the total area, respectively. The present soil resource mapping of the Eastern Ghats of Tamil Nadu is a pioneer study, which yielded valuable information on the soil in this region.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.449-460
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• 2020

Super-resolution is a technique used to reconstruct an image with low-resolution into that of high-resolution. Recently, deep-learning based super resolution has become the mainstream, and applications of these methods are widely used in the remote sensing field. In this paper, we propose a super-resolution method based on the deep back-projection network model to improve the satellite image resolution by the factor of four. In the process, we customized the loss function with the edge loss to result in a more detailed feature of the boundary of each object and to improve the stability of the model training using generative adversarial network based on Wasserstein distance loss. Also, we have applied the detail preserving image down-scaling method to enhance the naturalness of the training output. Finally, by including the modified-residual learning with a panchromatic feature in the final step of the training process. Our proposed method is able to reconstruct fine features and high frequency information. Comparing the results of our method with that of the others, we propose that the super-resolution method improves the sharpness and the clarity of WorldView-3 and KOMPSAT-2 images.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.2
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• pp.176-188
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• 2008

Anthropogenic emissions of nitrogen oxides and sulfur dioxide in Northeast Asia are of great concern because of their impact on air quality and atmospheric chemistry on regional and intercontinental scales. Satellite remote sensing based on DOAS (Differential Optical Absorption Spectroscopy) technique has been preferred to measure atmospheric trace species and to investigate their emission characteristics on regional and global scales. Absorption spectra obtained by the satellite-born instrument, SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) have been utilized to retrieve the information of $SO_2$ and $NO_2$ over Northeast Asia. $SO_2$ levels over Northeast Asia were in order of East China, Yellow Sea, South Sea and Korean Peninsula with mean vertical columns of $1.78({\pm}1.0){\times}10^{16}$, $1.11({\pm}0.67){\times}10^{16}$, $0.60({\pm}0.63){\times}10^{16}$, $0.71({\pm}0.65){\times}10^{16}\;molecules/cm^2$, respectively. $NO_2$ levels were in order of East China, Yellow Sea, Korean Peninsula, and South Sea with mean vertical columns of $1.2({\pm}0.56){\times}10^{16}$, $0.38({\pm}0.19){\times}10^{16}$, $0.48({\pm}0.28){\times}10^{16}$, $0.26({\pm}0.16){\times}10^{16}\;molecules/cm^2$, respectively. High levels of $SO_2$ and $NO_2$ were observed over East China, in particular in winter by the contribution of heating fuel combustion exhausts. The $SO_2$ and $NO_2$ levels over East China were the highest in January with 34% and 42% higher over the annual means. Low levels of $SO_2$ ranged over Korean peninsula, while $NO_2$ levels were relatively high, in particular in winter. The $SO_2$ and $NO_2$ levels over Yellow Sea were relatively higher compared to those over Korean peninsula and South Sea, which could be mainly attributed to their transport from East China.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1111-1124
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• 2021

Mudflats are crucial for understanding the ecological structure and biological function of coastal ecosystem because of its high primary production by microalgae. There have been many studies on measuring primary productivity of tidal flats for the estimation of organic carbon abundance, but it is relatively recent that optical remote sensing technique, particularly hyperspectral sensing, was used for it. This study investigates hyperspectral sensing of chlorophyll concentration on a tidal flat surface, which is a key variable in deriving primary productivity. The study site is a mudflat in Geunso bay, South Korea and field campaigns were conducted at ebb tide in April and June 2021. Hyperspectral reflectance of the mudflat surfaces was measured with two types of hyperspectral sensors; TriOS RAMSES (directionalsensor) and the Specim-IQ (camera sensor), and Normal Differenced Vegetation Index (NDVI) and Contiuum Removal Depth (CRD) were used to estimate Chl-a from the optical measurements. The validation performed against independent field measurements of Chl-a showed that both CRD and NDVI can retrieve surface Chl-a with R² around 0.7 for the Chl-a range of 0~150 mg/m² tested in this study.

• Journal of the Korean GEO-environmental Society
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• v.4 no.2
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• pp.15-25
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• 2003

Time Domain Refletometry, or TDR, is a remote sensing electrical measurement technique that has been used for many years to determine the spatial location and nature of various objects, especially in the United States of America and Australia at mining industry. Since early on 1990, the TDR techniques have been applied to the geotechnical engineering such as : deformation measurement of rock slope and landslide, monitoring of ground water content and ground water level change, investigation of ground contamination and its movement. The first application of this technique, in 1996, to the domestic area is to determine the possibility of ground settlement caused by subsidence from abandoned underground mines at the Tongri and Gosari in Gangwon-d. In this paper, through the results of analysed deformation data between conventional measurements and the TDR, it was concluded that the TDR technique is a useful instrumentation method for the prediction of ground deformation.

• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.493-503
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• 2005

In this study, the weight of evidence(WofE) technique based on GIS was applied to spatially estimate the groundwater yield characteristics at the Pocheon area In Gyunggi-do. The groundwater preservation depends on many hydro-geologic factors that include hydrologic data, land-use data, topographic data, geological map and other natural materials collected at the site, even with man-made things. All these data can be digitally processed and managed by GIS database. In the applied technique of WofE, the prior probabilities were estimated as the factors that affect the yield on lineament, geology, drainage pattern or river system density, landuse and soil. We calculated the value of the weight values, W+ and W-, of each factor and estimated the contrast value of it. Results by the groundwater yield characteristic computation using this scheme were presented feasibility map in the form of the posterior probability to the consideration of in-situ samples. It is concluded that this technique is regarded as one of the effective techniques for the feasibility mapping related to the estimation of groundwater-bearing potential zones and its spatial pattern.