• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1299-1312
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• 2019

As land remote sensing applications are expanding to the extraction of quantitative information, the importance of atmospheric correction is increasing. Considering the difficulty of atmospheric correction for land images, it should be applied when it is necessary. The quantitative information extraction and time-series analysis on biophysical variables in land surfaces are two major applications that need atmospheric correction. Atmospheric aerosol content and column water vapor, which are very dynamic in spatial and temporal domain, are the most influential elements and obstacles in retrieving accurate surface reflectance. It is difficult to obtain aerosol and water vapor data that have suitable spatio-temporal scale for high- and medium-resolution multispectral imagery. Selection of atmospheric correction method should be based on the availability of appropriate aerosol and water vapor data. Most atmospheric correction of land imagery assumes the Lambertian surface, which is not the case for most natural surfaces. Further BRDF correction should be considered to remove or reduce the anisotropic effects caused by different sun and viewing angles. The atmospheric correction methods of optical imagery over land will be enhanced to meet the need of quantitative remote sensing. Further, imaging sensor system may include pertinent spectral bands that can help to extract atmospheric data simultaneously.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.1011-1030
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• 2019

Remote sensing sensors used in satellites or aircrafts measure electromagnetic waves passing through the earth's atmosphere, and thus the information on the surface of the earth is affected as it is absorbed or scattered by the earth's atmosphere. Although satellites have different wavelength ranges and resolutions depending on the purpose of onboard sensors, in general, atmospheric correction must be made to remove the influence of the atmosphere in order to accurately measure the spectral signal of an object on the earth's surface. The purpose of atmospheric correction is to remove the atmospheric effect from remote sensing images to determine surface reflectivity values and to derive physical parameters of the surface. Until recently, atmospheric correction algorithms have evolved from image-based empirical methods or indirect methods using in-situ observation data to direct methods that numerically interpret more complex radiative transfer processes. This study analyzes the research records of atmospheric correction algorithms developed over the past 40 years, systematically establishes the current state of atmospheric correction technology and the results of major atmospheric correction algorithms and presents the current status and research trends of related technologies.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.31-41
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• 2016

Atmospheric correction as a preprocessing work should be performed to conduct accurately landcover/landuse classification using hyperspectral imagery. Atmospheric correction on airborne hyperspectral images was conducted and then the effect of atmospheric correction by comparing spectral reflectance characteristics before and after atmospheric correction for a few landuse classes was analyzed. In addition, land cover classification was first conducted respectively by the maximum likelihood method and the spectral angle mapper method after atmospheric correction and then the results were compared. Applying the spectral angle mapper method, the sea water area were able to be classified with the minimum of noise at the threshold angle of 4 arc degree. It is considered that object-based classification method, which take into account of scale, spectral information, shape, texture and so forth comprehensively, is more advantageous than pixel-based classification methods in conducting landcover classification of the coastal area with hyperspectral images in which even the same object represents various spectral characteristics.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• Journal of the Korean Society of Marine Environment & Safety
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• v.10 no.1 s.20
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• pp.1-7
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• 2004

RADARSAT is one of many possible data sources that can play an important role in marine surveillance including ship detection because radar sensors have the two primary advantages: all-weather and day or night imaging. However, atmospheric effects on SAR imaging can not be bypassed and any remote sensing image has various geometric distortions, In this study, radiometric and geometric calibrations for RADARSAT/SAT data are tried using SGX products georeferenced as level 1. Even comparison of the near vs. far range sections of the same images requires such calibration Radiometric calibration is performed by compensating for effects of local illuminated area and incidence angle on the local backscatter, Conversion method of the pixel DNs to beta nought and sigma nought is also investigated. Finally, automatic geometric calibration based on the 4 pixels from the header file is compared to a marine chart. The errors for latitude and longitude directions are 300m and 260m, respectively. It can be concluded that the error extent is acceptable for an application to open sea and can be calibrated using a ground control point.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1259-1268
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• 2021

The 2nd Geostationary Ocean Color Imager (GOCI-II) is the successor to the Geostationary Ocean Color Imager (GOCI), which employs one near-ultraviolet wavelength (380 nm) and eight visible wavelengths(412, 443, 490, 510, 555, 620, 660, 680 nm) and three near-infrared wavelengths(709, 745, 865 nm) to observe the marine environment in Northeast Asia, including the Korean Peninsula. However, the multispectral radiance image observed at satellite altitude includes both the water-leaving radiance and the atmospheric path radiance. Therefore, the atmospheric correction process to estimate the water-leaving radiance without the path radiance is essential for analyzing the ocean environment. This manuscript describes the GOCI-II standard atmospheric correction algorithm and its initial phase validation. The GOCI-II atmospheric correction method is theoretically based on the previous GOCI atmospheric correction, then partially improved for turbid water with the GOCI-II's two additional bands, i.e., 620 and 709 nm. The match-up showed an acceptable result, with the mean absolute percentage errors are fall within 5% in blue bands. It is supposed that part of the deviation over case-II waters arose from a lack of near-infrared vicarious calibration. We expect the GOCI-II atmospheric correction algorithm to be improved and updated regularly to the GOCI-II data processing system through continuous calibration and validation activities.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.981-996
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• 2018

In agriculture, remote sensing data using earth observation satellites have many advantages over other methods in terms of time, space, and efficiency. This study analyzed the changes of reflectance and vegetation index according to atmospheric correction of images before using satellite images in agriculture. Top OF Atmosphere (TOA) reflectance and surface reflectance through atmospheric correction were calculated to compare the reflectance of each band and Normalized Vegetation difference Index (NDVI). As a result, the NDVI observed from field measurement sensors and satellites showed a higher agreement and correlation than the TOA reflectance calculated from surface reflectance using atmospheric correction. Comparing NDVI before and after atmospheric correction for multi-temporal images, NDVI increased after atmospheric corrected in all images. garlic and onion cultivation area and forest where the vegetation health was high area NDVI increased more 0.1. Because the NIR images are included in the water vapor band, atmospheric correction is greatly affected. Therefore, atmospheric correction is a very important process for NDVI time-series analysis in applying image to agricultural field.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.101-110
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• 1999

Recently, there are a lot of studies to use a satellite image data in order to investigate a simultaneous change of a wide range area as a lake. However, in many cases of the water quality research there is one problem occured when extracting the water quality factors from the satellite image data because the atmosphere scattering exert a bad influence on a result of analysis. In this study, an attempt was made to select the relative atmospheric correction method, extract the water quality factors from the satellite image data. And also, the time-series analysis of the water quality factors was performed by using the multi-temporal image data.

• Proceedings of KOSOMES biannual meeting
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• 2004.05b
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• pp.49-52
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• 2004

RADARSAT is one of many possible data sources that can play an important role in marine surveillance including ship detection because radar sensors have the two primary advantages: all-weather and day or night imaging. However, atmospheric effects on SAR imaging can not be bypassed and any remote sensing image has various geometric distortions. In this study, radiometric and geometric calibrations for RADARSAT/SAR data are tried using SGX products georeferenced as level 1. For radiometric calibration, information on the magnitude of the radar backscatter coefficient of the imaged terrain is extracted from the processed image data. Conversion method of the pixel DNs to beta nought and sigma nought is also investigated Finally, automatic geometric calibration based on the header file is compared to a marine chart.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1029-1042
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• 2021

As part of the next-generation Compact Advanced Satellite 500 (CAS500) project, CAS500-4 is scheduled to be launched in 2025 focusing on the remote sensing of agriculture and forestry. To obtain quantitative information on vegetation from satellite images, it is necessary to acquire surface reflectance through atmospheric correction. Thus, it is essential to develop an atmospheric correction method suitable for CAS500-4. Since the absorption and scattering characteristics in the atmosphere vary depending on the wavelength, it is needed to analyze the sensitivity of atmospheric correction parameters such as aerosol optical depth (AOD) and water vapor (WV) considering the wavelengths of CAS500-4. In addition, as CAS500-4 has only five channels (blue, green, red, red edge, and near-infrared), making it difficult to directly calculate key parameters for atmospheric correction, external parameter data should be used. Therefore, thisstudy performed a sensitivity analysis of the key parameters (AOD, WV, and O₃) using the simulated images based on Sentinel-2 satellite data, which has similar wavelength specifications to CAS500-4, and examined the possibility of using the products of GEO-KOMPSAT-2A (GK2A) as atmospheric parameters. The sensitivity analysisshowed that AOD wasthe most important parameter with greater sensitivity in visible channels than in the near-infrared region. In particular, since AOD change of 20% causes about a 100% error rate in the blue channel surface reflectance in forests, a highly reliable AOD is needed to obtain accurate surface reflectance. The atmospherically corrected surface reflectance based on the GK2A AOD and WV was compared with the Sentinel-2 L2A reflectance data through the separability index of the known land cover pixels. The result showed that two corrected surface reflectance had similar Seperability index (SI) values, the atmospheric corrected surface reflectance based on the GK2A AOD showed higher SI than the Sentinel-2 L2A reflectance data in short-wavelength channels. Thus, it is judged that the parameters provided by GK2A can be fully utilized for atmospheric correction of the CAS500-4. The research findings will provide a basis for atmospheric correction of the CAS500-4 in the future.