• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.577-587
• /
• 2023

The utilization of crowdsourced spatial data has been actively researched; however, issues stemming from the uncertainty of data quality have been raised. In particular, when low-quality data is mixed into drone imagery datasets, it can degrade the quality of spatial information output. In order to address these problems, the study presents a methodology for automatically validating the geometric quality of crowdsourced imagery. Key quality factors such as spatial resolution, resolution variation, matching point reprojection error, and bundle adjustment results are utilized. To classify imagery suitable for spatial information generation, training and validation datasets are constructed, and machine learning is conducted using a radial basis function (RBF)-based support vector machine (SVM) model. The trained SVM model achieved a classification accuracy of 99.1%. To evaluate the effectiveness of the quality validation model, imagery sets before and after applying the model to drone imagery not used in training and validation are compared by generating orthoimages. The results confirm that the application of the quality validation model reduces various distortions that can be included in orthoimages and enhances object identifiability. The proposed quality validation methodology is expected to increase the utility of crowdsourced data in spatial information generation by automatically selecting high-quality data from the multitude of crowdsourced data with varying qualities.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_3
• /
• pp.2035-2046
• /
• 2021

Glacier movement speed is the most basic measurement for glacial dynamics research and is a very important indicator in predicting sea level rise due to climate change. In this study, the two-dimensional velocity measurements of Campbell Glacier located in Terra Nova Bay in East Antarctica were observed through the SAR offset tracking technique. For this purpose, domestic KOMPSAT-5 SAR satellite images taken on July 9, 2021 and August 6, 2021 were acquired. The Multi-kernel SAR offset tracking proposed through previous studies is a technique to obtain the optimal result that satisfies both resolution and precision. However, since offset tracking is repeatedly performed according to the size of the kernel, intensive computational power and time are required. Therefore, in this study, we strategically proposed a coarse-to-fine offset tracking approach. Through coarse-to-fine SAR offset tracking, it is possible to obtain a result with improved observation precision (especially, about 4 times in azimuth direction) while maintaining resolution compared to general offset tracking results. Using this proposed technique, a two-dimensional velocity measurements of Campbell Glacier were generated. As a result of analyzing the two-dimensional movement velocity image, it was observed that the grounding line of Campbell Glacier exists at approximately latitude -74.56N. The flow velocity of Campbell Glacier Tongue analyzed in this study (185-237 m/yr) increased compared to that of 1988-1989 (140-240 m/yr). And compared to the flow velocity (181-268 m/yr) in 2010-2012, the movement speed near the ground line was similar, but it was confirmed that the movement speed at the end of the Campbell Glacier Tongue decreased. However, there is a possibility that this is an error that occurs because the study result of this study is an annual rate of glacier movement that occurred for 28 days. For accurate comparison, it will be necessary to expand the data in time series and accurately calculate the annual rate. Through this study, the two-dimensional velocity measurements of the glacier were observed for the first time using the KOMPSAT-5 satellite image, a domestic X-band SAR satellite. It was confirmed that the coarse-to-fine SAR offset tracking approach of the KOMPSAT-5 SAR image is very useful for observing the two-dimensional velocity of glacier movements.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.785-791
• /
• 2020

The radiance measured from satellite has noise due to atmospheric effect. Atmospheric correction is the process of calculating surface reflectance by removing atmospheric effect and surface reflectance is calculated by the Radiative Transfer Model (RTM)-based Look-Up Table (LUT). In general, studies using a LUT make LUT for each channel with the same atmospheric and geometric conditions. However, atmospheric effect of atmospheric factors do not react sensitively in the same channel. In this study, the LUT for each channel of Korea Multi-Purpose SATellite (KOMPSAT)-3/3A was made under the same atmospheric·geometric conditions. And, the accuracy of the LUT was verified by using the simulated Top of Atmosphere radiation and surface reflectance in the RTM. As a result, the relative error of the surface reflectance in the blue channel that sensitive to the aerosol optical depth was 81.14% at the maximum, and 42.67% in the NIR (Near Infrared) channel.

• Korean Journal of Remote Sensing
• /
• v.27 no.4
• /
• pp.445-455
• /
• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Korean Journal of Remote Sensing
• /
• v.32 no.5
• /
• pp.529-538
• /
• 2016

This study, multispectral and hyperspectral sensors were utilized to use radiometric cross validation for the purpose of radiometric quality evaluation of a 'KOMPSAT-3'. Images of EO-1 Hyperion and Landsat-8 OLI sensors taken in PICS site were used. 2 sections that have 2 different types of ground coverage respectively were selected as the site of cross validation based on aerial hyperspectral sensor and TOA Reflectance. As a result of comparison between the TOA reflectance figures of KOMPSAT-3, EO-1 Hyperion and CASI-1500, the difference was roughly 4%. It is considered that it satisfies the radiological quality standard when the difference of figure of reflectance in a comparison to the other satellites is found within 5%. The difference in Blue, Green, Red band was approximately 3% as a comparison result of TOA reflectance. However the figure was relatively low in NIR band in a comparison to Landsat-8. It is thought that the relatively low reflectance is because there is a difference of band passes in NIR band of 2 sensors and in a case of KOMPSAT-3 sensor, a section of 940nm, which shows the strong absorption through water vapor, is included in band pass resulting in comparatively low reflectance. To overcome these conditions, more detailed analysis with the application of rescale method as Spectral Bandwidth Adjustment Factor (SBAF) is required.

• Korean Journal of Remote Sensing
• /
• v.24 no.6
• /
• pp.605-612
• /
• 2008

In this study, we estimate solar surface insolation (SSI) by using physical methods with MTSAT-1R data. SSI is regarded as crucial parameter when interpreting solar-earth energy system, climate change, and agricultural production predict application. Most of SSI estimation model mainly uses ground based-measurement such as pyranometer to tune the constructed model and to validate retrieved SSI data from optical channels. When compared estimated SSI with pyranometer measurements, there are some systemic differences between those instruments. The pyranometer data observed upward-looking hemispherical solid angle and distributed hourly measurements data which are averaged every 2 minute instantaneous observation. Whereas MTSAT-1R channels data are taken instantaneously images at fixed measurement time over scan area, and are pixel-based observation with a much smaller solid angle view. Those temporal discrepancies result from systemic differences can induce validation error. In this study, we adjust hour when estimate SSI to improve the retrieved accurate SSI.

• Korean Journal of Remote Sensing
• /
• v.17 no.3
• /
• pp.231-242
• /
• 2001

In 2-pass differential SAR interferometry(DInSAR), the topographic phase signature can be removed by using a digital elevation model(DEM) to isolate the contribution of deformation from interferometric phase. This method has an advantage of no unwrapping process, but applicability is limited by precision of the DEM used. The residual phase in 2-pass differential interferogram accounts for error of DEM used in the processing provided that no actual deformation exits. The objective of this paper is a preliminary study to improve DEM precision using low precision DEM and 2-pass DInSAR technique, and we applied the 2-pass DInSAR technique to Asan area. ERS-1/2 tandem complex images and DTED level 0 DEM were used for DInSAR, and the precision of resulting DEM was estimated by a 1:25,000 digital map. The input DEM can be improved by simply adding the DInSAR output to the original low precision DEM. The absolute altitude error of the improved DEM is 9.7m, which is about the half to that of the original DTED level 0 data. And absolute altitude error of the improved DEM is better than that from InSAR technique, 15.8m. This approach has an advantage over the InSAR technique in efficiently reducing layover effects over steep slope region. This study demonstrates that 2-pass DInSAR can also be used to improve DEM precision.

• Korean Journal of Remote Sensing
• /
• v.11 no.3
• /
• pp.49-60
• /
• 1995

Features of geological lineaments generally play an important role at the data interpretation concerned geological processes, mineral exploration or natural hazard risk estimation. However, there are intrinsically discordances between lineaments-related features extracted from surficial geological syrvey and those from satellite imagery;nevertheless, any data set contained those information should not be considred as less meaningful within their own task. For the purpose of effective utilization task of extracted lineaments, the mathematical scheme, based on fuzzy set theory, for practical integration of various types of rasterized data sets is studied. As a real application, the geological map named Homyeong sheet(1:50,000) and the Landset TM imageries covering same area were used, and then lineaments-related data sets such as lineaments on the geological map, lineaments extracted from a false-color image composite satellite, and major drainage pattern were utilized. For data fusion process, fuzzy membership functions of pixel values in each data set were experimentally assigned by percentile, and then fuzzy algebraic sum operator was tested. As a result, integrated lineaments by this well-known operator are regarded as newly-generated reasonable ones. Conclusively, it was thought that the implementation within available GISs, or the stand-alone module for general applications of this simple scheme can be utilized as an effective scheme can be utilized as an effective scheme for further studies for spatial integration task for providing decision-supporting information, or as a kind of spatial reasoning scheme.

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

• Korean Journal of Remote Sensing
• /
• v.36 no.6_1
• /
• pp.1435-1447
• /
• 2020

The seasonal variations in weather parameters can significantly affect the atmospheric transmission characteristics. Herein, we propose a novel augmented multiple regression algorithm for the accurate estimation of atmospheric transmittance and solar irradiance over highly localized areas. The algorithm employs 1) adaptive atmospheric model selection using measured meteorological data and 2) multiple linear regression computation augmented with the conventional application of MODerate resolution atmospheric TRANsmission (MODTRAN). In this study, the proposed algorithm was employed to estimate the solar irradiance over Taean coastal area using the 2018 clear days' meteorological data of the area, and the results were compared with the measurement data. The difference between the measured and computed solar irradiance significantly improved from 89.27 ± 48.08σ W/㎡ (with standard MODTRAN) to 21.35 ± 16.54σ W/㎡ (with augmented multiple regression algorithm). The novel method proposed herein can be a useful tool for the accurate estimation of solar irradiance and atmospheric transmission characteristics of highly localized areas with various weather conditions; it can also be used to correct remotely sensed atmospheric data of such areas.