• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.681-692
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• 2018

This study was performed to select the significant wavelengths for predicting the chlorophyll content of grafted cucumber seedlings using hyperspectral images. The visible and near-infrared (VNIR) images and the short-wave infrared images of cucumber cotyledon samples were measured by two hyperspectral cameras. A correlation coefficient spectrum (CCS), a stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine significant wavelengths. Some wavelengths at 501, 505, 510, 543, 548, 619, 718, 723, and 727 nm were selected by CCS, SMLR, and PLS as significant wavelengths for estimating chlorophyll content. The results from the calibration models built by SMLR and PLS showed fair relationship between measured and predicted chlorophyll concentration. It was concluded that the hyperspectral imaging technique in the VNIR region is suggested effective for estimating the chlorophyll content of grafted cucumber leaves, non-destructively.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.667-669
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• 2003

A bio-optical model was developed specific for turbid and shallow waters. Special studies were carried out to estimate absorption and scattering properties as well as backscattering probability of suspended matter. The inversion of bio-optical model allows for direct retrieval of turbidity and chlorophyll- a from the visible-near infrared (VNIR) range sensor. Time-series satellite imagery from ASTER AM-1 sensor, were used to monitor the Laguna de Bay water quality condition. Spatial distribution of temperature for the lake was extracted from the thermal infrared (TIR) sensor. Corresponding field surveys were conducted to parameterize the bio -optical model. In-situ measurements include suspended particle and chlorophyll-a concentrations profiles from nephelometric devices and processing of water samples. Hyperspectral measurements were used to validate results of the bio -optical model and satellite- based estimation. This study provides a theoretical basis and a practical illustration of applying space- based measurements on an operational basis.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.371-382
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• 2005

We present a processing system for the Atmospheric Infrared Sounder (AIRS) sounding suite onboard Aqua satellite. With its unprecedented 2378 channels in IR bands, AIRS aims at achieving the sounding accuracy of radiosonde (1 K in 1-km layer for temperature and $10\%$ in 2-km layer for humidity). The core of the processor is the International MODIS/AIRS Processing Package (IMAPP) that performs the geometric and radiometric correction for generation of Level 1 brightness temperature and Level 2 geophysical parameters retrieval. The processor can produce automatically from received raw data to Level 2 geophysical parameters. As we process the direct-broadcast data almost for the first time among the AIRS direct-broadcast community, a special attention is paid to understand and verify the Level 2 products. This processor includes sub-systems, that is, the near real time validation system which made the comparison results with in-situ measurement data, and standard digital information system which carry out the data format conversion into GRIdded Binary II (GRIB II) standard format to promote active data communication between meteorological societies. This processing system is planned to encourage the application of geophysical parameters observed by AIRS to research the aqua cycle in the Korean peninsula.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.239-251
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• 1999

The elimination process of cloud-contaminated pixels is one of important steps before obtaining the accurate parameters of land and ocean surface from AVHRR imagery. We developed a 6step threshold method to detect the cloud-contaminated pixels from NOAA-14/AVHRR datime imagery over land using different combination of channels. This algorithm has two phases : the first is to make a cloud-free characteristic data of land surface using compositing techniques from channel 1 and 5 imagery and a dynamic threshold of brightness temperature, and the second is to identify the each pixel as a cloud-free or cloudy one through 4-step threshold tests. The merits of this method are its simplicity in input data and automation in determining threshold values. The threshold of infrared data is calculated through the combination of brightness temperature of land surface obtained from AVHRR imagery, spatial variance of them and temporal variance of observed land surface temperature. The method detected the could-comtaminated pixels successfully embedded inthe NOAA-14/AVHRR daytime imagery for the August 1 to November 30, 1996 and March 1 to July 30, 1997. This method was evaluated through the comparison with ground-based cloud observations and with the enhanced visible and infrared imagery.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.97-107
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• 2017

In the field of automatic door, the infrared rays and microwave sensor are much used as the important components in charge of the motor's operation control of open and close through the incoming signal of object recognition. In case of existing system that the sensor of the infrared rays and microwave are applied to the automatic door, there are many malfunctions by the infrared rays and visible rays of the sun. Because the automatic doors are usually installed outside of building in state of exposure. The environmental change by temperature difference occurs the noise of object recognition detection signal. With this problem, the hardware fault that the detection sensor is unable to follow the object moving rapidly within detection area makes the sensing blind spot. This fault should be improved as soon as possible. Because It influences safety of passengers who use the automatic doors. This paper conducted an experiment to improve the detection area by installing extra ultrasonic sensor besides existing detection sensor. So, this paper realize the computing circuit and detection algorithm which can correctly and rapidly process the access route of objects moving fast and the location area of fixed obstacles by applying detection and advantages of ultrasonic signal to the automatic doors. With this, It is proved that the automatic door applying ultrasonic sensor is improved detection area of blind spot sensing through field test and improvement plan is proposed.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.275-284
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• 2024

Monitoring crop growth changes and water content is crucial in the agricultural sector. This study utilized drones equipped with Short Wavelength Infrared (SWIR) sensors, sensitive to moisture changes, to observe soybeans' growth and water content variations. We confirmed that as soybeans grow more vigorously, their water content increases and differences in irrigation levels lead to decreases in vegetation and moisture indices. This suggests that waterlogging slows down soybean growth and reduces water content, highlighting the importance of detailed monitoring of vegetation and moisture indices at different growth stages to enhance crop productivity and minimize damage from waterlogging. Such monitoring could also preemptively detect and prevent the adverse effects of moisture changes, such as droughts, on crop growth. By demonstrating the potential for early diagnosis of moisture stress using drone-based SWIR sensors, this research suggests improvements in the efficiency of large-scale crop management and increases in yield, contributing to agricultural production.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1299-1308
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• 2023

Forest fires are becoming more frequent and larger around the world due to climate change. Remote sensing such as satellite images can be used as an alternative or assistance data because it reduces various difficulties of field survey. Forest burn severity (differenced normalized burn ratio, dNBR) is calculated through the difference in normalized burn ratio (NBR) before and after a forest fire. The images used in the NBR formula are based on Landsat's near-infrared (NIR) and short-wavelength infrared (SWIR) bands. South Korea's satellite images don't have a SWIR band. So domestic studies related to forest burn severity calculated dNBR using overseas images or indirectly using the normalized difference vegetation index (NDVI) using South Korea's satellite images. Therefore, in this study, dNBR was calculated by substituting the mid-wavelength infrared (MWIR) band of Kompsat-3A (K3A) instead of the SWIR band in the NBR formula. The results were compared with the dNBR results obtained through Landsat which is the standard for dNBR formula. As a result, it was shown that dNBR using K3A's MWIR band has a wider range of values and can be expressed in more detail than dNBR using Landsat's SWIR band. Therefore, it is considered that K3A images will be highly useful in surveying burn areas and severity affected by forest fires. In addition, this study used the K3A's MWIR band images degraded to 30 m. It is considered that much better results will be obtained if a higher-resolution MWIR band is used.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.889-900
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• 2017

In this study, we analyzed the paper about NIR (Near-Infrared) remote sensing data and systematically summarized the research and application fields of NIR. To do this, we conducted a case study on the use of NIR in domestic journals, and SCI journals in the field of technology development for the last 5 years. After selection, a total of 281 journals were analyzed. For the statistical analysis, the classification was divided into subclasses and the dominant research trends were examined. As a result, the researchers who wrote the papers made the highest score of about 60% or more at university. In the field of application, 50% of land, 30% of environment, and 11% of disaster were distributed on SCI journals. In Korea, on the other hand, 55% of land, 24% of environment and 10% of disasters were distributed. In addition, 17% of the national land management and 8% of the geological / natural resources. Disaster observation using NIR was used for landslide, drought, weather disaster and flood. In particular, meteorological disasters are a result of study on Asian dust. However, there were no results of forest fire detection in Korea. Considering the domestic situation, it seems necessary to carry out additional and active research on this. It is expected that this statistical analysis data will be used as basic data to help expand the NIR technology development and utilization field in Korea in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.93-100
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• 2007

The mobile robots to rescue a life in a disaster area and to explore planets demand high mobility as well as recognition of the environment. To avoid unknown obstacles exactly in unknown environment, accurate sensing is required. This paper proposes a sensor fusion to recognize unknown obstacles accurately by using low-cost sensors. Ultrasonic sensors and infrared sensors are used in this paper to avoid obstacles. If only one of these sensors is used alone, it is not useful fer the mobile robots to complete their tasks in the real world since the surrounding environment in the real world is complex and composed of many kinds of materials. So infrared sensor may not recognize transparent or reflective obstacles and ultrasonic sensor may not recognize narrow obstacles, far example, columns of small diameter. Therefore, I selected six ultrasonic sensors and five infrared sensors to detect obstacles. Then, I fused ultrasonic sensors with infrared sensors in order that both advantages and disadvantages of each sensor are utilized together. In fusing sensors, fuzzy algorithm is used to cope with the uncertainties of each sensor. TAMRY which is terrain-adaptive mobile robot is used as the mobile robot for experiments.