• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.518-525
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• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.175-182
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• 2006

Because of the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions of agricultural croplands, grasslands, and disturbed forests by multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been a primary problem to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement (GER-2600) and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Red-edge near 700nm and shortwave IR near 2,200nm are more sensitive to the fraction of dry vegetation. The use of hyperspectral data would allow us for better separation between bare soils and other surfaces covered by dry vegetation during the leaf-off season.

• 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.

• Proceedings of the KSRS Conference
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• v.2
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• pp.619-622
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• 2006

Atmospheric correction is one of critical procedures to extract quantitative information related to biophysical variables from hyperspectral data. In this study, we attempted to generate the water vapor contents image from hyperspectral data itself and developed the atmospheric correction algorithm for EO-1 Hyperion data using pre-calculated atmospheric look-up-table (LUT) for fast processing. To apply the new atmospheric correction algorithm, Hyperion data acquired June 3, 2001 over Seoul area is used. Reflectance spectrums of various targets on atmospheric corrected Hyperion reflectance images showed the general spectral pattern although there must be further development to reduce the spectral noise.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.11
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• pp.634-643
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• 2003

This paper presents a method for detecting skin tumors on chicken carcasses using hyperspectral images. It utilizes both fluorescence and reflectance image information in hyperspectral images. A detection system that is built on this concept can increase detection rate and reduce processing time, because the procedure for detection can be simplified. Chicken carcasses are examined first using band ratio FCM information of fluorescence image and it results in candidate regions for skin tumor. Next classifier selects the real tumor spots using PCA components information of reflectance image from the candidate regions. For the real world application, real-time processing is a key issue in implementation and the proposed method can accommodate the requirement by using a limited number of features to maintain the low computational complexity. Nevertheless, it shows favorable results and, in addition, uncovers meaningful spectral bands for detecting tumors using hyperspectral image. The method and findings can be employed in implementing customized chicken tumor detection systems.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.413-420
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• 2012

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.107-116
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• 2016

Airborne or spaceborne remote sensing can increase the efficiency of seabed material surveys compared with field surveying using a vessel. For the same seabed material, the optical remote sensing image shows variation in the reflectance depending on the water depth, which is due to the absorption and scattering by the water column. This study suggests a correction procedure to use the hyperspectral image for seabed material analysis. The study is conducted in the coastal area from Sacheonjin Port to Gyungpo Beach in Gangwon-do. The hyperspectral image is acquired using the CASI-1500 sensor. The diffuse attenuation coefficient is estimated for each band through regression models between the water reflectance and depth. Then, the coefficient is applied to each band of the image. As a result, the completely corrected image can be interpreted for a deeper area, although the interpretable area is very shallow without water column correction. Additionally, the water column corrected image shows decreased variation of reflectance with various water depths.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.665-675
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• 2014

In this paper spectral characteristics and spectral patterns of pine wilt disease at different development stage were analyzed in Geoje-do where the disease has already spread. Ground-based hyperspectral imaging containing hundreds of wavelength band is feasible with continuous screening and monitoring of disease symptoms during pathogenesis. The research is based on an hyperspectral imaging of trees from infection phase to witherer phase using a ground based hyperspectral camera within the area of pine wilt disease outbreaks in Geojedo for the analysis of pine wilt disease. Hyperspectral imaging through hundreds of wavelength band is feasible with a ground based hyperspectral camera. In this research, we carried out wavelength band change analysis on trees from infection phase to witherer phase using ground based hyperspectral camera and comparative analysis with major vegetation indices such as Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (reNDVI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index 2 (ARI2). As a result, NDVI and reNDVI were analyzed to be effective for infection tree detection. The 688 nm section, in which withered trees and healthy trees reflected the most distinctions, was applied to reNDVI to judge the applicability of the section. According to the analysis result, the vegetation index applied including 688 nm showed the biggest change range by infection progress.

• Spatial Information Research
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• v.23 no.2
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• pp.31-38
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• 2015

Remote sensing data is used to increasing efficiency on benthic cover type survey. Satellite and aerial imagery has variance of reflectance by water column effect even if bottom is consisted with same cover type and condition. This study tried to analyze advances of surveying extent and accuracy through water column correction of CASI-1500 hyperspectral image. Study area is coast of Gangneung city, South Korea where benthic environment is rapidly changing with bleaching of coral reef. Water column correction coefficient was estimated using regression models between water reflectance ($R_W$) and depth for sand bottom then the coefficients were applied to whole image. The results shows that expanded interpretable depth from 6-7m to 15m and decreased variation of reflectance by depth. Additionally, water column corrected reflectance image shows 13%p increased accuracy on benthic cover type classification.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.213-222
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• 2008

Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.