• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.291-304
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• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.

• Smart Media Journal
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• v.2 no.4
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• pp.60-65
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• 2013

In this paper, we propose a high-resolution disparity map generation method using a low-resolution time-of-flight (TOF) depth camera and color camera. The TOF depth camera is efficient since it measures the range information of objects using the infra-red (IR) signal in real-time. It also quantizes the range information and provides the depth image. However, there are some problems of the TOF depth camera, such as noise and lens distortion. Moreover, the output resolution of the TOF depth camera is too small for 3D applications. Therefore, it is essential to not only reduce the noise and distortion but also enlarge the output resolution of the TOF depth image. Our proposed method generates a depth map for a color image using the TOF camera and the color camera simultaneously. We warp the depth value at each pixel to the color image position. The color image is segmented using the mean-shift segmentation method. We define a cost function that consists of color values and segmented color values. We apply a weighted average filter whose weighting factor is defined by the random walk probability using the defined cost function of the block. Experimental results show that the proposed method generates the depth map efficiently and we can reconstruct good virtual view images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.1-9
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• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.211-221
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• 2021

The Rabbit grass(Trifolium Repens, call it 'Clover') is a representative harmful plant of lawn, and it starts growing earlier than lawn, forming a water pipe on top of the lawn and hindering the photosynthesis and growth of the lawn. As a result, in competition between lawn and clover, clover territory spreads, but lawn is damaged and dried up. Damage to the affected lawn area will accelerate during the rainy season as well as during the plant's rear stage, spreading the area where soil is exposed. Therefore, the restoration of damaged lawn is causing psychological stress and a lot of economic burden. The purpose of this study is to distinguish clover which is a representative harmful plant on lawn, to identify the distribution of damaged areas due to the spread of clover, and to review of changes in vegetation before and after the eradication of clover. For this purpose, a time series analysis of three vegetation indices calculated based on images of convergence Drone with RGB(Red Green Blue) and BG-NIR(Near Infra Red)sensors was reviewed to identify the separation between lawn and clover for selective eradication, and the distribution of damaged lawn for recovery plan. In particular, examined timeseries changes in the ecology of clover before and after the weed-whacking by manual and brush cutter. And also, the method of distinguishing lawn from clover was explored during the mid-year period of growth of the two plants. This study shows that the time series analysis of the MGRVI(Modified Green-Red Vegetation Index), NDVI(Normalized Difference Vegetation Index), and MSAVI(Modified Soil Adjusted Vegetation Index) indices of drone-based RGB and BG-NIR images according to the growth characteristics between lawn and clover can confirm the availability of change trends after lawn damage and clover eradication.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.401-410
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• 2020

In this study, a real-time Tactical UAS position compensation system based on image information developed to compensate for the weakness of location navigation information during GPS signal interference and jamming / spoofing attack is described. The Tactical UAS (KUS-FT) is capable of automatic flight by switching the mode from GPS/INS integrated navigation to DR/AHRS when GPS signal is lost. However, in the case of location navigation, errors accumulate over time due to dead reckoning (DR) using airspeed and azimuth which causes problems such as UAS positioning and data link antenna tracking. To minimize the accumulation of position error, based on the target data of specific region through image sensor, we developed a system that calculates the position using the UAS attitude, EO/IR (Electric Optic/Infra-Red) azimuth and elevation and numerical map data and corrects the calculated position in real-time. In addition, function and performance of the image information based real-time UAS position compensation system has been verified by ground test using GPS simulator and flight test in DR mode.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.3
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• pp.240-245
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• 2013

Each pixel of InfraRed(IR) sensor differently responds to IR light as time elapses or the sensor on/off operation is repeated. As a result, the quality of IR sensor image is deteriorated, and therefore NUC(Non-uniformity Correction) is periodically needed for IR sensor. In this paper, in order to perform NUC in the Satellite, on-board V-grooved blackbody is designed with a baffle so that the emissivity of black body is to be higher than 0.995 as well as the temperature deviation is less than $1^{\circ}C$ in the range of the infrared wave length from 3.3 to $5.2{\mu}m$. To check its performance, the emissivity and the surface temperature of the blackbody by TRT(Transfer Reference Thermometer) and IR Micrometer scanner are measured, respectively. From the results, black body design is verified and the uncertainty of NUC is estimated through the measurement results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.370-380
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• 2002

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.65.1-65.1
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• 2012

MIRIS (Multipurpose Infra-Red Imaging System) is the first Korean Infrared Space Telescope developed by KASI (Korea Astronomy and Space Science Institute), and is the main payload of STSAT-3 (Science and Technology Satellite-3). The FM (fight model) of MIRIS has been recently completed, and various performance tests have been made to measure system parameters such as readout noise, system gain, linearity, and dark current. Final thermal-vacumm test of the MIRIS and the vibration test of the electronics box have been performed. Band response tests showed good agreement with the initial design requirements. No significant dark difference was measured within the expected temperature variation range during observation in orbit. Using Pa-alpha band from a uniform source, the readout noise and system gain were measured by mean variance test. To obtain uniform flat image, flat fielding tests were made for each band, and the data will be compared to that obtained in orbit for calibration. The final version of MIRIS FM will be delivered in March, and it will be integrated into the satellite system for the AIT (Assembly Integration, Test) procedure. The launch of MIRIS is expected in November 2012.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.180-191
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• 2016

When up-scaling algorithms for visible light images are applied to infrared (IR) images, they rarely work because IR images are usually blurred. In order to solve such a problem, this paper proposes an up-scaling algorithm for IR images. We employ adaptive dynamic range encoding (ADRC) as a simple classifier based on the observation that IR images have weak details. Also, since human visual systems are more sensitive to edges, our algorithm focuses on edges. Then, we add pre-processing in learning phase. As a result, we can improve visibility of IR images without increasing computational cost. Comparing with Anchored neighborhood regression (A+), the proposed algorithm provides better results. In terms of just noticeable blur, the proposed algorithm shows higher values by 0.0201 than the A+, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.94-101
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• 2009

In this paper, we propose design of hardware based on a high speed digital signal processor (DSP) and a field programmable gate array (FPGA) for real-time suppression of fixed pattern noise (FPN) using hardware neural networks (HNN) in cooled infrared focal plane array (IRFPA) imaging system FPN appears a limited operation by temperature in observable images which applies to non-uniformity correction for infrared detector. These have very important problems because it happen serious problem for other applications as well as degradation for image quality in our system Signal processing architecture for our system operates reference gain and offset values using three tables for low, normal, and high temperatures. Proposed method creates virtual tables to separate for overlapping region in three offset tables. We also choose an optimum tenn of temperature which controls weighted values of HNN using mean values of pixels in three regions. This operates gain and offset tables for low, normal, and high temperatures from mean values of pixels and it recursively don't have to do an offset compensation in operation of our system Based on experimental results, proposed method showed improved quality of image which suppressed FPN by change of temperature distribution from an observational image in real-time system.