• Korean Journal of Optics and Photonics
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• v.30 no.2
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• pp.37-47
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• 2019

A reflecting omnidirectional optical system with four spherical and aspherical mirrors, for use with long-wavelength infrared light (LWIR) for night surveillance, is proposed. It is designed to include a collecting pseudo-Cassegrain reflector and an imaging inverse pseudo-Cassegrain reflector, and the design process and performance analysis is reported in detail. The half-field of view (HFOV) and F-number of this optical system are $40-110^{\circ}$ and 1.56, respectively. To use the LWIR imaging, the size of the image must be similar to that of the microbolometer sensor for LWIR. As a result, the size of the image must be $5.9mm{\times}5.9mm$ if possible. The image size ratio for an HFOV range of $40^{\circ}$ to $110^{\circ}$ after optimizing the design is 48.86%. At a spatial frequency of 20 lp/mm when the HFOV is $110^{\circ}$, the modulation transfer function (MTF) for LWIR is 0.381. Additionally, the cumulative probability of tolerance for the LWIR at a spatial frequency of 20 lp/mm is 99.75%. As a result of athermalization analysis in the temperature range of $-32^{\circ}C$ to $+55^{\circ}C$, we find that the secondary mirror of the inverse pseudo-Cassegrain reflector can function as a compensator, to alleviate MTF degradation with rising temperature.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.263-270
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• 2018

Land surface temperature is known to be an important factor in understanding the interactions of the ground-atmosphere. However, because of the large spatio-temporal variability, regular observation is rarely made. The existing land surface temperature is observed using satellite images, but due to the nature of satellite, it has the limit of long revisit period and low accuracy. In this study, in order to confirm the possibility of replacing land surface temperature observation using satellite imagery, images acquired by TIR (Thermal Infrared) sensor mounted on UAV (Unmanned Aerial Vehicle) are used. The acquired images were transformed from JPEG (Joint Photographic Experts Group) to TIFF (Tagged Image File Format) format and orthophoto was then generated. The DN (Digital Number) value of orthophoto was used to calculate the actual land surface temperature. In order to evaluate the accuracy of the calculated land surface temperature, the land surface temperature was compared with the land surface temperature directly observed with an infrared thermometer at the same time. When comparing the observed land surface temperatures in two ways, the accuracy of all the land covers was below the measure accuracy of the TIR sensor. Therefore, the possibility of replacing the satellite image, which is a conventional land surface temperature observation method, is confirmed by using the TIR sensor mounted on UAV.

• The Plant Pathology Journal
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• v.32 no.4
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• pp.300-310
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• 2016

Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (${\geq}10^6cfu/ml$) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (${\Phi}PSII$) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII ($F_v/F_m$) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.293-300
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• 2000

Automated greenhouse production system often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to nutrient stresses. Thermal images were obtained from lettuce, cucumber, and pepper plants. Plants were placed in growth chamber to provide relatively constant growth environment. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. In a case of the both N deficiency and excess, the leaf temperatures of cucumber were $2^{\circ}C$ lower than controlled temperature. The leaf temperature of cucumber was $2^{\circ}C$ lower than controlled temperature only when it was under N excess stress. For the potassium deficiency or excess stress, the leaf temperaures of cucumber and hot pepper were $2^{\circ}C$ lower than controls, respectively. The phosphorous deficiency stress dropped the leaf temperatures of cucumber and hot pepper $2^{\circ}C$ and $1.5^{\circ}C$ below than controls. However, the leaf temperature of lettuce did not change. It was possible to detect the changes in leaf temperature by infrared thermography when subjected to nutrition stress. Since the changes in leaf temperatures were different each other for plants and kinds of stresses, however, it is necessary to add a nutrient measurement system to a plant-growth monitoring system using thermography.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.397-400
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• 2007

For mapping the distribution of heavy metals in the mining area, field spectroscopy and hyperspectral remote sensing were used in this study. Although heavy metals are spectrally featureless from the visible to the short wave infrared range, possible variations in spectral signal due to heavy metals bound onto minerals can be explained with the metal binding reaction onto the mineral surface. Variations in the spectral absorption shapes of lattice OH and oxygen on the mineral surface due to the combination of heavy metals were surveyed over the range from 420 to 2400 nm. Spectral parameters such as peak ratio and peak area were derived and statistically linked to metal concentration levels in the streambed samples collected from the dry stream channels. The spatial relationships between spectral parameters and concentrations of heavy metals were yielded as well. Based on the observation at a ground level for the relationship between spectral signal and metal concentration levels, the spectral parameters were classified in a hyperspectral image and the spatial distribution patterns of classified pixels were compared with the product of analysis at the ground level. The degree of similarity between ground dataset and image dataset was statistically validated. These techniques are expected to support assessment of dispersion of heavy metal contamination and decision on optimal sampling point.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.393-402
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• 2003

The Multi-Spectral Camera (MSC) is the payload of KOMPSAT-2 which is designed for earth imaging in optical and near-infrared region on a sun-synchronous orbit. The telescope in the MSC is a Ritchey-Chretien type with large aperture. The telescope structure should be well stabilized and the optical alignment should be kept steady so that best images can be achieved. However, the MSC is exposed to adverse thermal environment on the orbit which can give impacts on optical performance. Solar incidence can bring non-uniform temperature rise on the telescope tube which entails unfavorable thermal distortion. Three ways of preventing the solar radiation were proposed, which were installing external mechanical shield, internal shield, and maneuvering the spacecraft. After trade-off study, internal sun shield was selected as a practical and optimal solution to minimize the effect of the solar radiation. In addition, detailed designs of the structure and sunshield were produced and analyses have been performed. The results were assessed to verify their impacts to the image quality. It was confirmed that the internal sunshield complies with the requirements and would improve image quality.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.5
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• pp.84-91
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• 2008

The spectral radiance received by a remote sensor at a given temperature and wavelength region is consisted of the self-emitted component directly from the object surface, the reflected component of the solar irradiation at the object surface, and the scattered component by the atmosphere without ever reaching the object surface. The IR image of a ship is mainly affected by location, meteorological condition(atmosphere temperature, wind direction and velocity, humidity etc.), atmospheric transmittance, solar position and ship surface temperature etc. Computer simulations for prediction of the IR signatures of ships are very useful to examine the effects of various meteorological conditions. In this paper, we have acquired the IR signature for different meteorological conditions by using two different computer programs. The numerical results show that the IR image contrast as compared to the background sea considering the atmosphere temperature and wind velocity.

• Journal of Aerospace System Engineering
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• v.15 no.6
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• pp.42-49
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• 2021

To develop a high-resolution electro-optical/infrared (EO/IR) payload to be mounted on a high-speed and performance fighter aircraft in an external POD for acquiring daytime and nighttime image information on tactical targets, simulations, including flight environments and maneuvers, should be performed. Such simulations are pertinent to predicting the performance of several variables, such as aerodynamic force and inertia load acting on the payload. This paper describes the development of a flight data acquisition and analysis system based on flight simulation software (SW) for mission simulation of super-maneuverability fighter equipped with EO/IR payload. The effectiveness of the system is verified through comparison with actual flight data. The proposed flight data acquisition and analysis system based on FlightGear can be used as an M&S tool for system performance analysis in the development of the EO/IR payload.