• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.369-377
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• 2011

Since well-calibrated satellite data is critical for their applications, calibration and validation of COMS science data was one of the key activities during the IOT. COMS MI radiometric calibration process was divided into two phases according to the out-gassing of the sensor: calibrations of the visible (VI) and infrared (IR) channels. Different from the VIS calibration, the calibration steps for the IR channels followed additional processes to secure their radiometric performances. Primary calibration steps of the IR were scan mirror emissivity correction, midnight effect compensation, slope averaging and 1/f noise compensation after a nominal calibration. First, the scan mirror emissivity correction was conducted to compensate the variability of the scan mirror emissivity driven by the coating material on the scan mirror. Second, the midnight effect correction was performed to remove unreasonable high spikes of the slope values caused by the excessive radiative sources during the local midnight. After these steps, the residual (difference between the previous slope and the given slope) was filtered by a smoothing routine to eliminate the remnant random noises. The 1/f noise compensation was also carried out to filter out the lower frequency noises caused from the electronics in the Imager. With through calibration processes during the entire IOT period, the calibrated IR data showed excellent performances.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.187-190
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• 2007

The Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of ocean-color around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI has been designed to provide multi-spectral data to detect, monitor, quantify, and predict short term changes of coastal ocean environment for marine science research and application purpose. The target area of GOCI observation covers sea area around the Korean Peninsula. Based on the nonlinear radiometric model, the GOCI calibration method has been derived. The nonlinear radiometric model for GOCI will be validated through ground test. The GOCI radiometric calibration is based on on-board calibration devices; solar diffuser, DAMD (Diffuser Aging Monitoring Device). In this paper, the GOCI radiometric error propagation is analyzed. The radiometric model error due to the dark current nonlinearity is analyzed as a systematic error. Also the offset correction error due to gain/offset instability is considered. The radiometric accuracy depends mainly on the ground characterization accuracies of solar diffuser and DAMD.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.92-95
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• 2008

The Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of ocean-color around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI has been designed to provide multi-spectral data to detect, monitor, quantify, and predict short term changes of coastal ocean environment for marine science research and application purpose. The target area of GOCI observation covers sea area around the Korean Peninsula. Based on the nonlinear radiometric model, the GOCI calibration method has been derived. The radiometric model of GOCI has been validated through radiometric ground test. From this ground test result, GOCI radiometric model has been changed from second order to third order. In this paper, the radiometric test performed to evaluate the radiometric nonlinearity is described and the GOCI radiometric error propagation is analyzed. The GOCI radiometric calibration is based on onboard calibration devices; solar diffuser, DAMD (Diffuser Aging Monitoring Device). The radiometric model error due to the dark current nonlinearity is considered as a systematic error. Also the offset correction error due to gain/offset instability is considered. The radiometric accuracy depends mainly on the ground characterization accuracies of solar diffuser and DAMD.

• Proceedings of the KSRS Conference
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• v.1
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• pp.60-63
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• 2006

Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of oceancolor around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. In this paper radiometric calibration concept of the GOCI is introduced. The GOCI radiometric response is modeled as a nonlinear system in order to reflect a nonlinear characteristic of detector. In this paper estimation approaches for radiometric parameters of GOCI model are discussed. For the GOCI, the offset signal depends on each spectral channel because dark current offset signal is a function of integration time which is different from channel to channel. The offset parameter can be estimated by using offset signal measurements for two integration time setting is described.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.57-73
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• 1997

This paper is related to the correction of radiometric distortions induced by topographic relief. RADARSAT SAR image data were obtained over the mountainous area near southern part of Seoul. Initially, the SAR data was geometrically corrected and registered to plane rectangular coordinates so that each pixel of the SAR image has known topographic parameters. The topographic parameters (slope and aspect) at each pixel position were calculated from the digital elevation model (DEM) data having a comparable spatial resolution with the SAR data. Local incidence angle between the incoming microwave and the surface normal to terrain slope was selected as a primary geometric factor to analyze and to correct the radiometric distortions. Using digital maps of forest stands, several fields of rather homogeneous forest stands were delineated over the SAR image. Once the effects of local incidence angle on the radar backscatter were defined, the radiometric correction was performed by an empirical fuction that was derived from the relationship between the geometric parameters and mean radar backscatter. The correction effects were examined by ground truth data.

• Journal of Astronomy and Space Sciences
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• v.17 no.2
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• pp.295-308
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• 2000

OSMI(Ocean Scanning Multi-Spectral Imager) raw image data(Level 0) were acquired and radiometrically corrected. We have applied two methods, using solar & dark calibration data from OSMI sensor and comparing with the SeaWiFS data, to the radiometric correction of OSMI raw image data. First, we could get the values of the gain and the offset for each pixel and each band from comparing the solar & dark calibration data with the solar input radiance values, calculated from the transmittance, BRDF (Bidirectional Reflectance Distribution Function) and the solar incidence angle($\beta$, $\theta$) of OSMI sensor. Applying this calibration data to OSMI raw image data, we got the two odd results, the lower value of the radiometric corrected image data than the expected value, and the Venetian Blind Effect in the radiometric corrected image data. Second, we could get the reasonable results from comparing OSMI raw image data with the SeaWiFS data, and get a new problem of OSMI sensor.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.87-98
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• 2010

For generating accurate land cover map over the whole Korean Peninsula, post-mosaic classification method is desirable in large area where multiple image data sets are used. We try to derive an optimal mosaic method of multi-temporal Landsat ETM+ scenes for the land cover classification over the Korea Peninsula. Total 65 Landsat ETM+ scenes were acquired, which were taken in 2000 and 2001. To reduce radiometric difference between adjacent Landsat ETM+ scenes, we apply three relative radiometric correction methods (histogram matching, 1st-regression method referenced center image, and 1st-regression method at each Landsat ETM+ path). After the relative correction, we generated three mosaic images for three seasons of leaf-off, transplanting, leaf-on season. For comparison, three mosaic images were compared by the mean absolute difference and computer classification accuracy. The results show that the mosaic image using 1st-regression method at each path show the best correction results and highest classification accuracy. Additionally, the mosaic image acquired during leaf-on season show the higher radiance variance between adjacent images than other season.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.285-289
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• 1999

A processing system to produce cloud-free composite image data set was developed. In the process, a fine geometric correction based on orbit parameters and ground control points and radiometric correction based on 6S code are applied. Presently, by using AVHRR image data received at Tokyo, Okinawa, Ulaanbaatar and Bangkok, data set of 10 days composite images covering almost whole Asian region.

• Proceedings of the KSRS Conference
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• v.1
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• pp.184-187
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• 2006

The radiometric correction is prerequisite to derive both land and ocean surface properties from optical remote sensing data. Radiometric calibration of remotely sensed data has traditionally been accomplished by means of vicarious ground calibration techniques. The purpose of this study is to calibrate the radiometric characteristic of Airborne Multispectral Scanner (AMS) by field campaign. In order to calibrate the AMS data, four different spectral tarps which are 3.5%, 23%, 35%, and 53% were validated by GER-3700 that is the surface reflectance measurement equipment and were utilized. After validation of the spectral tarps, each reflectance from the spectral tarps was compared with Digital Number (DN) value of AMS. There was very high correlation between tarp reflectance and DN value of AMS so that radiometric calibration of AMS data has been accomplished by those results. The calibrated AMS data were validated with in-situ measured reflectance data from artificial and natural target. Also QuickBird image data were used for verifying the results of AMS radiometric calibration. This presentation discusses the results of the above tests.

• 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.