• 대한원격탐사학회지
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• 제3권2호
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• pp.121-141
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• 1987

Many techniques of image processing have been developed to analyse more precisely geological information obtained from satellites. SPOT, which is a recent project in France, will furnish stereoscopic image, with good resolution of surfaces(20m $\times$ 20m or 10m $\times$ 10m), and give altitudes(DEM) which can be restored automatically. One of the researches for the exploitation of this data, intends to recognize and distinguish automatically the geomorphological forms, containing important geological information from DEM. Along which the information obtained obtained from image processing, it will play an important role in the understanding of the surface of the terrain. This study was carried out in collaboration with University of Paris-6 and Ecole National des Sciences G$\'{e}$ographiques(Institute G$\'{e}$ographique National of France: IGN).

• 대한원격탐사학회지
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• 제19권3호
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• pp.247-253
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• 2003

The mainly used technique to correct satellite images with geometric distortion is to develop a mathematical relationship between pixels on the image and corresponding points on the ground. Polynomial models with various transformations have been designed for defining the relationship between two coordinate systems. GCP based geometric correction has peformed overall plane to plane mapping. In the overall plane mapping, overall structure of a scene is considered, but local variation is discarded. The Region with highly variant height is rectified with distortion on overall plane mapping. To consider locally variable region in satellite image, TIN-based rectification on a satellite image is proposed in this paper. This paper describes the relationship between GCP distribution and rectification model through experimental result and analysis about each rectification model. We can choose a geometric correction model as the structural characteristic of a satellite image and the acquired GCP distribution.

• 대한원격탐사학회지
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• 제33권4호
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• 대한공간정보학회지
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• 제4권2호
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• pp.23-33
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• 1996

광역수계에서 현재의 수질평가 방법은 시간과 장비 등의 제약으로 오염물질 분포, 이동 및 전반적인 수질현황을 파악하기가 어렵기 때문에 최근에는 대상수역의 수질을 동시적이고 공간적으로 측정을 할 수 있는 원격탐측 적용 연구가 증가추세에 있다. 따라서 본 연구에서는 위성 원격탐측기법으로 호수 수질을 관측하고자 1995년 6월 20일과 1995년 3월 18일에 Landsat 5호 위성의 대청호 상공 통과시간에 맞춰 대청호에서 부영양화 관련 수질인자를 측정하여 위성데이터와 수질 실측치간의 상관관계 분석 및 회귀모델을 유도하였고 모델의 정밀도를 검증하였다 연구결과 TM데이터로부터 수질에 관한 많은 정보를 얻을 수 있었는데, 투명도, 탁도, 부유물질 및 클로로필은 높은 상관성을 보였으나 분광특성이 뚜렷하지 않은 총인, 총질소는 원격탐측 적용이 어려운 것으로 나타났다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.393-395
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• 2003

The changes of DEMs(Digital Elevation Models) will be detected to correct the information of DEMs and/or to get the information about themselves. This study suggests the evaluation of DEM using correlation coefficient value between the target and the reference DEMs for detect the changes.

• 대한원격탐사학회지
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• 제36권1호
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• pp.41-53
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• 2020

The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• 대한원격탐사학회지
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• 제29권3호
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• pp.307-314
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• 2013

The Bidirectional Reflectance Distribution (BRD) effect is critical to interpret the surface information using remotely sensed data. This effect was caused by geometric relationship between sensor, target and solar that is inevitable effect for data of optical sensor. To remove the BRD effect, semi-empirical BRDF models are widely used. It is faster to calculate than physical models and demanded less observation than empirical models. In this study, Ross-Li kernel and Roujean kernel were used respectively in National Aeronautics and Space Administration (NASA) and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) that are used to compare each other. The semi-empirical model consists of three parts which are isotropic, geometric and volumetric scattering. Each part contained physical kernel and empirical coefficients that were calculated by statistical method. Red and NIR channel of SPOT/VEGETATION product were used to compute Nadir BRDF Adjusted Reflectance (NBAR) over East Asia area from January 2009 to December 2009. S1 product was provided by VITO that was conducted atmospheric correction using Simplified Method of Atmospheric Correction (SMAC). NBAR was calculated using corrected reflectance of red and NIR. Previous study has revealed that Roujean geometric kernel had unphysical values in large zenith angles. We extracted empirical coefficients in three parts and normalized reflectance to compare both BRDF models. Two points located forest in Korea peninsular and bare land in Gobi desert were selected for comparison. As results of time series analysis, both models showed similar reflectance change pattern and reasonable values. Whereas in case of empirical coefficients comparison, different changes pattern of values were showed in isotropic coefficients.

• 대한원격탐사학회지
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• 제34권1호
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• pp.45-74
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• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.