• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.757-765
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• 2002

This paper analyzes three theories for land degradation assessment and internationl/domestic methods for land degradation monitoring and assessment. Under the guidance of absolute degradation thought, this paper proposes the technological framework for monitoring and appraising cultivated land degradation based on the 3S technique. We can apply 3S technique and analyze the nature, the environmental, the social, and the economic elements which influence the land utilization and degradation synthetically, to set up the indicator system of the cultivated land degradation monitoring and assessment based on 3S technique; to propose the degradation information extraction methods based on 3S technique; to create the quantitative assessment model and method for land degradation; to analyze the ecological environment response of land use and degradation quantitatively; and to propose the measure, policy and suggestion for solving the land degradation problem from the point of view of land utilization.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1791-1796
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• 2005

Semi-shield tunneling is one of the propulsion construction methods used to lay pipes underground between two pits named 'entrance' and 'destination', respectively. Usually a simple composition, such as 'a fiducial target at the entrance+a total station (TS)+a target on the machine', is used to confirm the planned course. However, unavoidable curved sections are present in small-sized pipe lines, which are laid after implementation of a road system, for public works such as waterworks, sewer, electrical power, and gas and communication networks. Therefore, if the planned course has a curved section, it is difficult to survey the course with the abovementioned simple composition. This difficulty could be solved by using the multiple total stations (MTS), which attaches the cross type linear LED target to oneself. The MTS are disposed to where each TS can detect the LED target at the other TS or the base point or the machine. And the accurate relative positions between each MTS and target are calculated from measured data. This research proposes the relative and absolute coordinate calculation algorithm by using three MTS to measure a curved course with 20m curvature at 30m maximum distance, and verifies the algorithm experimentally.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.2
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• pp.125-135
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• 2001

The research on Harmful Algal Blooms is generally in progress through field work, such as the naked eye and sampling. It was difficult to forecast exactly the course, from appearance of red tide to disappearance, with the established ways of investigation and analysis. Accordingly it is need to analyze environmental factors in time and space, the appearance of red tide and the path of its migration by more objective and scientific methods. In this study the remote sensing was used to diminish damage from the occurrence of red tide. Such as a temperature change of sea water and a change of tidal currents, the major cause for red tide. The probed data were utilized. The technique for forecast of red tide phenomenon on the south coast was researched by analyzing the cause of red tide, pollutant flowed from landand the possibility of application of the technique was showed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.225-227
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• 2003

Traditional photogrammetry and satellite image rectification technique have been developed based on control-points for many decades. These techniques are driven from linked points in image space and the corresponding points in the object space in rigorous colinearity or coplanarity conditions. Recently, digital imagery facilitates the opportunity to use features as well as points for images rectification. These implementations were mainly based on rigorous models that incorporated geometric constraints into the bundle adjustment and could not be applied to the new high-resolution satellite imagery (HRSI) due to the absence of sensor calibration and satellite orbit information. This research is an attempt to establish a new Line Based Transformation Model (LBTM), which is based on linear features only or linear features with a number of ground control points instead of the traditional models that only use Ground Control Points (GCPs) for satellite imagery rectification. The new model does not require any further information about the sensor model or satellite ephemeris data. Synthetic as well as real data have been demonestrated to check the validity and fidelity of the new approach and the results showed that the LBTM can be used efficiently for rectifying HRSI.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.617-627
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• 2010

Although many researches for heat island study have been developed, there is little attempt to link the findings to actual and hypothetical scenarios of urban developments which would help to mitigate the Urban Heat Island (UHI) in cities. The aim of this paper is to analyze the UHI at urban area with different geometries, land use, and environmental factors, and emphasis on the influence of different geometric and environmental parameters on ambient air temperature. In order to evaluate these effects, the parameters of (i) Air pollution (i.e. Aerosol Optical Thickness (AOT)), (ii) Green space Normalized Difference Vegetation Index (NDVI), (iii) Anthropogenic heat (AH) (iv) Building density (BD), (v) Building height (BH), and (vi) Air temperature (Ta) were mapped. The optimum operational scales between Heat Island Intensity (HII) and above parameters were evaluated by testing the strength of the correlations for every resolution. The best compromised scale for all parameters is 275m resolution. Thus, the measurements of these parameters contributing to heat island formation over the study areas of Hong Kong were established from mathematical relationships between them and in combination at 275m resolution. The mathematical models were then tabulated to show the impact of different percentages of parameters on HII. These tables are useful to predict the probable climatic implications of future planning decisions.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.260-263
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• 2005

This study was carried out to investigate the effects of spatial resolutions on digital image for detecting pine trees damaged by pine wilt disease. Color infrared images taken from PKNU-3 multispectral airborne photographing system with a spatial resolution of 50cm was used as a basic data. Further test images with spatial resolutions of 1m, 2m and 4m were made from the basic data to test the detecting capacity on each spatial resolution. The test was performed with visual interpretation both on mono and stereo modus and compared with field surveying data. It can be conclude that it needs less than 1m spational resolutions or 1m spatial resolutions with stereo pair in order to detect pine trees damaged by pine wilt disease.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.253-261
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• 2013

Current surveying and spatial information technology incorporates information and communication technology and is user friendly. However, it is not convenient to use in the field because a connection to a computer, such as a laptop, tablet PC, or desktop PC, is needed to obtain the survey results and the coordinates of the surveyed points. To solve this problem, we developed an app that can display surveyed data on a map and the current survey results through a connection between a total station and smartphone using a Bluetooth wireless communication device. The app allows users to perform field work simultaneously with office work in the field, because it consists of Bluetooth, closed traverse survey, current status survey, and coordinate conversion modules. The proposed app should increase user convenience and the operational capability of the total station in the field.