• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.334-336
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• 2003

Satellite and airborne hyperspectral sensor images are suitable for investigating the vegetation state in agricultural land. However, image data obtained by an optical sensor inevitably includes mixels caused by high altitude observation. Therefore, mixel analysis method, which estimates both the pure spectra and the coverage of endmembers simultaneously, is required in order to distinguish the qualitative spectral changes due to the chlorophyll quantity or crop variety, from the quantitative coverage change. In this paper, we apply our agricultural independent component analysis (ICA) model to an airborne hyperspectral sensor image, which includes noise and fluctuation of coverage, and estimate pure spectra and the mixture ratio of crop and soil in agricultural land simultaneously.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.631-639
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• 2008

This study presents an approach to synthesize hyperspectral images of lower resolution at a higher resolution using the high resolution images acquired from a sensor of commercial satellites. The proposed method was applied to the reconstruction of EO-1 Hyperion images using the images acquired from IKONOS sensor. Based on the FitPAN-Mod pansharpening technique (Lee, 2008b), the hyperspectral images of 30m resolution were reconstructed at 1m resolution of IKONOS panchromatic image. In this study, the synthesized hyperspectral images of 50 bands, whose wavelengths range in the wavelength of panchromatic sensor, were generated from the three stages of high resolution reconstruction using FitPAN-Mod. The experimental results show that the proposed method effectively integrates the spatial detail of the panchromatic modality as well as the spectral detail of the hyperspectral one into the synthesized image. It indicates the proposed method has a potential as a technique to produce alternative images for the images that would have been observed from a hyperspectral sensor at the high resolution of commercial satellite images.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.4
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• pp.484-491
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• 2019

General Hyperspectral Image Sensor acquires an image of line form such as a thin rectangle shape because of using 1D array Push Broom or Whisk Broom scanning method. A special mechanism is required for a Hyperspectral Image Sensor to operate for nadir and slant range. To design the mechanism, the characteristics of the flight motion and the overlap rate between consecutive frames were analyzed. Also, system requirements were proposed through modeling and simulation.

• Journal of Environmental Science International
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• v.30 no.2
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• pp.173-184
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• 2021

Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.10-13
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• 2004

Hyperspectral data has a great advantage to classify various surface materials that are spectrally similar. In this study, we attempted to classify man-made materials in urban area using Hyperion data. Hyperion imagery of Seoul was initially processed to minimize radiometric distortions caused by sensor and atmosphere. Using color aerial photographs. we defined seven man-made surfaces (concrete, asphalt road. railroad, buildings, roof, soil, shadow) for the classification in Seoul. The hyperspectral data showed the potential to identify those manmade materials that were difficult to be classified by multispectral data. However. the classification of road and buildings was not quite satisfactory due to the relatively low spatial resolution of Hyperion image. Further, the low radiometric quality of Hyperion sensor was another limitation for the application in urban area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.7-14
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• 2007

Atmospheric correction is one of critical procedures to extract quantitative information related to biophysical variables from hyperspectral imagery. Most atmospheric correction algorithms developed for hyperspectral data have been based upon atmospheric radiative transfer (RT) codes, such as MODTRAN. Because of the difficulty in acquisition of atmospheric data at the time of image capture, the complexity of RT model, and large volume of hyperspectral data, atmospheric correction can be very difficult and time-consuming processing. In this study, we attempted to develop an efficient method for the atmospheric correction of EO-1 Hyperion data. This method uses the pre-calculated look-up-table (LUT) for fast and simple processing. The pre-calculated LUT was generated by successive running of MODTRAN model with several input parameters related to solar and sensor geometry, radiometric specification of sensor, and atmospheric condition. Atmospheric water vapour contents image was generated directly from a few absorption bands of Hyperion data themselves and used one of input parameters. This new atmospheric correction method was tested on the Hyperion data acquired on June 3, 2001 over Seoul area. Reflectance spectra of several known targets corresponded with the typical pattern of spectral reflectance on the atmospherically corrected Hyperion image, although further improvement to reduce sensor noise is necessary.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.185-190
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• 2014

The purpose of this research is to extract spectral reflectance characteristics of concretes through basic experiment on concrete specimens and site experiment on actual concrete structures using a field portable spectrometer and a VNIR hyperspectral sensor. A spectrometer (GER-3700) and a VNIR hyperspectral camera (AisaEagle VNIR Hyperspectral Camera) were utilized for extracting spectral characteristics of concrete specimens. Concretes normally show similar patterns that have correlation above 80%, while the high-strengthened concretes display very different results from the normal-strength concretes. We also made a certain conclusion in the laboratory experiment on concrete specimens that both the spectrometer and the VNIR camera vary in spectral reflectance depending on concrete strengths.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.658-663
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• 2018

Current measuring methods for air quality are based on ground measurement networks and satellite data. New methods of collecting evidence with advanced sensors are needed because current methods have limitations in collecting evidence for the illegal emission of air pollutants at narrow areas or specific sites. This study analyzed the possibility of using an ultraviolet hyperspectral sensor to measure the concentration of nitrogen dioxide and sulfur dioxide. Two types of spectra were used: simulated spectra for gases with various concentrations using a radiative transfer model and observed spectra for each gas for a concentration. To understand the possibility of using a hyperspectral sensor, the differences between the simulated spectra and the observed spectra were analyzed, and the variation of simulated spectra were then analyzed according to the concentration. The results showed good agreement between observed spectra and simulated spectra. In addition, the absorption depth at specific wavelengths in the simulated spectra had a very strong correlation with the gas concentration. The gas concentration could be estimated using the hyperspectral sensor. In the future, validation would be needed to estimate the gas concentration through observations of various concentrations of gases using a hyperspectral sensor.