• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.100-104
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• 2013

In this paper, we present an optimal threshold setting method for flame detection of infrared thermal image. Conventional infrared flame detection methods used fixed intensity threshold to segment candidate flame regions and further processing is performed to decide correct flame detection. So flame region segmentation step using the threshold is important processing for fire detection algorithm. The threshold should be change in input image depends on camera types and operation conditions. We have analyzed the conventional thresholds composed of fixed-intensity, average, standard deviation, maximum value. Finally, we extracted that the optimal threshold value is more than summation of average and standard deviation, and less than maximum value. it will be enhance flame detection rate than conventional fixed-threshold method.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Journal of Korean Physical Therapy Science
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• v.2 no.4
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• pp.771-778
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• 1995

The purpose of this study was to determine the effects of linear polarized near-infrared ray radiation on experimental pain threshold in healthy adult subjects. Thirty healthy adult subjects were divided into 5 groups: 1) group 1, super lizer radiation at 0 % ; 2) group 2, super lizer radiation at 10 % ; 3) group 3, super lizer radiation at 20 % ; 4) group 4, super lizer radiation at 40 % ; 5) group 5, super lizer radiation at 80 %. The polarized near-infrared ray radiation was applied on LI 4 point of subject's right hand at a fixed time for 30 second. Experimental pain threshold were measured with electrical current on the right hand at 5 intervals for each radiation: 1) pretreat; 2) posttreat ; 3) posttreat of 1/2 hr ; 4) posttreat of 1 hr ; and 5) posttreat of 2 hrs. Data were analyzed using analyses of variance with repeated measures for pain threshold. No significant interaction between power output and time for pain threshold was found. Significant effects of power output and time for experimental pain threshold were found. Significant increase was noted in experimental pain threshold in group 4 and group 5 at posttreat, posttreat of 1/2, 1 hr and 2 hrs as a result of the applications of the polarized near-infrared ray radiation. This study indicate that polarized near-infrared ray radiation with above 40 % of power output increases pain threshold, thus possibly increasing options in choosing radiation output for treating pateint with pain. Further study is needed to compare the effects of these radiation in patients with clinical pain.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.91-96
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• 2014

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adapts according to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed threshold method since surveillance environment does not change, once the system installed. But it needs a adaptive threshold method as requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behavior of flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithm and conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detection rate and false detection is reduced by 20% comparing to the conventional method.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.403-409
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• 2003

Since SPOT4 satellite contained VEGETATION 1 sensor launched, the noise in VEGETATION data was occasionally arisen a difficulty for the data traitement. Blind line noise types were studied in VEGETATION-l short wave infrared channel(SWIR). In order to provide a precis product, the procedure for removing this noise is strongly recommended. In the case that the blind values are clearly distinguished from contamination-free values a simple threshold method was applied, while a changeable threshold method was used for the blind value mixed with contamination-free values. New algorithm presented in this study is consists of two method for each type of SWIR blind. After removing blind line, there were again some residual pixels of blind, because the threshold is not determinated sufficiently low. Lower threshold could remove the blind line as well as the contamination-free pixels. Nevertheless, the results showed a good qualitative improvement as compared with other algorithm.

• 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.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.149-152
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• 2007

The detection of yellow sand dust using satellite has been utilized from various bands from ultraviolet to infrared channels. Among them, Infrared channels have an advantage of detecting aerosols over high reflecting surface as well as during nighttime. Especially, brightness temperature difference between 11 and 12{\mu}m(BTD) was often used to distinguish between water cloud and yellow sand, because Ice and liquid water particles preferentially absorb longer wavelengths while aerosol particles preferentially absorb shorter wavelengths. We have found that the BTD significantly depends on surface temperature, emissivity, and zenith angle and thereby the threshold of BTD. In order to overcome these problems, we have constructed the background brightness temperature threshold of BTD and then subtracted it from BTD. Along with this, we utilized high temporal coverage of geostationary satellite, MTSAT-1R, to verify the reliability of the retrieved signal in conjunction with forecasted wind information. The statistical score test illustrated that this newly developed algorithm showed a promising result for detecting mineral dust by reducing the errors in the current BTD method.

• Journal of Sensor Science and Technology
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• v.20 no.4
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• pp.230-233
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• 2011

In this paper, highly-efficient optical gating in a junction device based on vanadium dioxide($VO_2$) thin film grown by a sol-gel method was investigated as a gate terminal of a three-terminal device using infrared light with a wavelength of ~1554.6 nm. Due to the photoinduced phase transition, the threshold voltage of the $VO_2$ junction device, at which the device current abruptly jumps, could be tuned with a sensitivity of ~96.5 V/W by adjusting the optical power of the infrared light directly illuminating the device. Compared with the tuning efficiency of the previous device fabricated using $VO_2$ thin film deposited by a pulsed laser deposition method, the threshold voltage of this device could be tuned by ~76.8 % at an illumination power of ~39.8 mW resulting in a tuning efficiency of ~1.930 %/mW, which is ~4.9 times larger than the previous device.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.