• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• The Journal of Engineering Geology
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• v.23 no.4
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• pp.399-407
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• 2013

Some groundwater in Korea contains high U concentrations, especially where two-mica granite occurs in the Daejeon area. The elemental U in the two-mica granite is lower than that in normal granites elsewhere in the world, and U-minerals have yet to be reported in the two-mica granite in the Daejeon area. This study focuses on investigating the occurrence of U-minerals serving as the U source in groundwater. In situ gamma ray spectrometry and mineralogical analyses using EPMA were performed. U-count anomalies were identified in a granitic dyke and in hydrothermally altered granite. Uraniferous granitic dykes occur along the contact zone between the two-mica granite and mica-schist. The uraniferous parts within the two-mica granite are developed in the hydrothermally altered zone, which contains numerous quartz veinlets within a fracture zone. Hydrothermal alteration is dominated by potassic and prophylitic alteration. Uraninite is a common U-mineral in granitic dykes and hydrothermally altered granite. Coffinite and uranophane occur in the hydrothermally altered granite. All of these U-minerals are commonly accompanied by hydrothermal alteration minerals such as muscovite, chlorite, epidote, and calcite. It is concluded that granitic dyke and hydrothermally altered granite are the main source rocks of U in groundwater.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.279-300
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• 2001

Data mining plays an important role in knowledge discovery process and usually various existing algorithms are selected for the specific purpose of the mining. Currently, data mining techniques are actively to the statistics, business, electronic commerce, biology, and medical area and currently numerous algorithms are being researched and developed for these applications. However, in a long run, only a few algorithms, which are well-suited to specific applications with excellent performance in large database, will survive. So it is reasonable to focus our effort on those selected algorithms in the future. This paper classifies about 30 existing algorithms into 7 categories - association rule, clustering, neural network, decision tree, genetic algorithm, memory-based reasoning, and bayesian network. First of all, this work analyzes systematic hierarchy and characteristics of algorithms and we present 14 criteria for classifying the algorithms and the results based on this criteria. Finally, we propose the best algorithms among some comparable algorithms with different features and performances. The result of this paper can be used as a guideline for data mining researches as well as field applications of data mining.