• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.113-119
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• 2003

Building information is extremely important for many applications within the urban environment. Sufficient techniques and user-friendly tools for information extraction from remotely sensed imagery are urgently needed. This paper presents an automatic and manual approach for extracting footprints of buildings in urban areas from airborne Light Detection and Ranging (LIDAR) data. First a digital surface model (DSM) was generated from the LIDAR point data. Then, objects higher than the ground surface are extracted using the generated DSM. Based on general knowledge on the study area and field visits, buildings were separated from other objects. The automatic technique for extracting the building footprints was based on different window sizes and different values of image add backs, while the manual technique was based on image segmentation. A comparison was then made to see how precise the two techniques are in detecting and extracting building footprints. Finally, the results were compared with manually digitized building reference data to conduct an accuracy assessment and the result shows that LIDAR data provide a better shape characterization of each buildings.

• Spatial Information Research
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• v.12 no.4 s.31
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• pp.351-370
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• 2004

At present, environmental monitoring of linear infrastructure is based mainly on field sampling. The 'integrated mapping' approach has received only limited attention from field scientists. The increased environmental regulation of corridor targets has required remote sensing research to develop a sensor or technique for targets ranging from 15 m to 100 m in swath width. In an attempt to identify the optimal remote sensing system for linear targets, an overview is provided of the application requirements and the technology currently available. The relative limitation of traditional remote sensing systems in such a linear application is briefly discussed. It is noted that airborne video could provide, in a cost-effective manner, information required for a very narrow and long strip target utilising the narrow view angle and dynamic stereo coverage. The value of this paper is warranted in proposing a new concept of video infrastructure monitoring as a future research direction in the recognition of sensor characteristics and limitations.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.341-369
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• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.177-180
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• 2008

Many researches have been tried to extract building models and created a 3D cyber city from LiDAR data. In this paper, the approach of extracting complex building by using airborne LiDAR data combined with airborne orthoimagery has been performed. The pseudo-building elevations were derived from modified discrete return LiDAR data. Based on information property of the pseudo-height, building features could be extracted. The results of this study indicated the improvement of building extraction.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.1
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• pp.32-39
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• 2013

Although space-borne satellites are useful in obtaining information all around the world, they cannot observe at a suitable time and place. In order to overcome these limitations, an airborne remote sensing system was developed in this study. It is composed of a SAR sensor and a thermal infrared sensor. Additionally GPS, IMU, and thermometer/hygrometer were attached to the plane for radiometric and geometric calibration. The brightness of SAR image varies depending on surface roughness, and capillary waves on the sea surface, which are easily generated by sea winds, induce the surface roughness. Thus, sea surface wind can be estimated using the relationship between quantified SAR backscattering coefficient and the sea surface wind. On the other hand, thermal infrared sensor is sensitive to measure object's temperature. Sea surface temperature is obtained from the thermal infrared sensor after correcting the atmospheric effects which are located between sea surface and the sensor. Using these two remote sensing sensors mounted on airplane, four test flights were carried out along the west coast of Korea. The obtained SAR and thermal infrared images have shown that these images were useful enough to monitor coastal environment and estimate marine meteorology data.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1901-1922
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• 2021

Since most large earthquakes occur by reactivation of preexisting active faults, it is important to understand the locations and characteristics of active faults in terms of earthquake hazard research and earthquake disaster prevention. Recently, several remote sensing techniques are broadly used for lineament analysis performed prior to field surveys in active fault surveys. The aim of this paper is introducing simple principles and application examples of each remote sensing technique (satellite remote sensing, airborne remote sensing, InSAR, LiDAR) widely used for active fault investigation. This paper also explains the analytical methods for the slope break generated by fault activity based on GIS and the horizontal displacement of the strike-slip fault. In discussion, we would like to discuss the problems and solutions on making DEM based on aerial photography, and a new developed technique (RRIM) to overcome the problems of DEM based on aerial LiDAR. Understanding remote sensing techniques used for active fault investigation and utilizing appropriate methods depending on the situation and limitations of each remote sensing technique are important for effective active fault investigation.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.388-393
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• 2002

Remote sensing data is collected and analyzed to enhance understanding of the terrestrial surface. Since Landsat satellite was launched in 1972, many researches using multispectral data has been achieved. Recently, with the availability of airborne and satellite hyperspectral data, the study on hyperspectral data are being increased. It is known that as the number of spectral bands of high-spectral resolution data increases, the ability to detect more detailed cases should also increase, and the classification accuracy should increase as well. In this paper, we classified the hyperspectral and multispectral data and tested the classification accuracy. The MASTER(MODIS/ASTER Airborne Simulator, 50channels, 0.4~13$\mu$m) and Landsat TM(7channels) imagery including Yeong-Gwang area were used and we adjusted the classification items in several cases and tested their classification accuracy through statistical comparison. As a result of this study, it is shown that hyperspectral data offer more information than multispectral data.

• 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.

• 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.

• Proceedings of the KSRS Conference
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• v.2
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• pp.684-687
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• 2006

Airborne imagery must be precisely orthorectified to be used as geographical information data. GPS/INS (Global Positioning System/Inertial Navigation System) and LIDAR (LIght Detection And Ranging) data were employed to automatically orthorectify airborne images. In this study, 154 frame airborne images and LIDAR vector data were acquired. LIDAR vector data were converted to raster image for employing as reference data. To derive images with constant brightness, flat field correction was applied to the whole images. The airborne images were geometrically corrected by calculating internal orientation and external orientation using GPS/INS data and then orthorectified using LIDAR digital elevation model image. The precision of orthorectified images was validated using 50 ground control points collected in arbitrary selected five images and LIDAR intensity image. In validation results, RMSE (Root Mean Square Error) was 0.365 smaller then two times of pixel spatial resolution at the surface. It is possible that the derived mosaicked airborne image by this automatic orthorectification method is employed as geographical information data.