• 한국위성정보통신학회논문지
• /
• 제8권4호
• /
• pp.100-104
• /
• 2013

본 논문은 열영상 기반의 화염 검출을 위한 기존의 문턱치 설정 기법들을 분석하고 최적 문턱치 설정 방안을 제시한다. 기존의 열영상 기반의 화염검출 알고리즘들은 보통 고정 문턱치를 이용하여 화염 후보영역을 추출하고 후처리를 통해 화염 검출을 최종 판정하므로 화염 후보영역의 결정 과정은 최종 화재 검출 결과에 많은 영향을 준다. 따라서 카메라의 종류나 운영 환경에 따라 입력 영상의 대비와 밝기의 변화가 발생하기 때문에 화염 검출 문턱치는 입력영상의 특성에 연동하여 설정되어져야 한다. 따라서 최적 문턱치 설정 방안을 제시하기 위해 고정 명암도, 평균값, 표준편차 및 최대값을 이용한 문턱치 설정 기법들을 비교 분석하였다. 결론적으로 최적 문턱치는 평균과 표준편차의 합보다 크며 최대값 보다는 작은 값으로 설정 한다면 화염 검출 정확도가 기존 고정 문턱치 방식에 비해 크게 개선될 것으로 기대된다.

• Journal of the Korean Physical Society
• /
• 제73권11호
• /
• pp.1644-1649
• /
• 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%.

• 대한물리치료과학회지
• /
• 제2권4호
• /
• pp.771-778
• /
• 1995

편광 근적외선 조사가 실험적 통증역치에 영향을 미치는지를 규명하기 위해서 건강한 성인의 합곡혈에 여러 단계의 출력량으로 편광 근적외선을 30초간 조사하고 시간 경과에 따라 실험적 통증역치를 측정하여 분석하였다. 실험 결과 출력량 0 %인 대조군과 10 % 및 20 %의 출력량에서는 실험적 통증역치의 유의한 변화가 없었다. 그러나 출력량 40 %군의 실험적 통증역치는 편광 근적외선 조사전 $3.85{\pm}0.51mA$에서 조사 직후 $4.77{\pm}0.87mA$, 조사 후 30분 $5.57{\pm}0.98mA$, 조사 후 1시간 $5.68{\pm}1.06mA$, 조사 후 2시간 $5.34{\pm}0.96mA$로 증가되었고, 출력량 80 %군에서도 실험적 통증역치가 조사전 $3.87{\pm}0.92mA$에서 조사 직후 $4.45{\pm}0.62mA$, 조사 후 30분 $4.91{\pm}0.51mA$, 조사 후 1시간 $4.93{\pm}0.62mA$, 조사 후 2시간 $5.55{\pm}1.11mA$로 증가하였다. 실험적 통증역치는 편광 근적외선 조사 직후부터 2시간까지 유의하게 증가하였다. 40 % 출력량과 80 % 출력량의 실험적 통증역치는 유의한 차이가 없었고, 출력량과 시간의 상호작용효과는 없었다. 건강인의 실험적 통증역치를 대상으로 한 본 연구 결과를 통증이 있는 환자에게 직접 적용하기는 어렵다고 생각하며 앞으로 환자를 대상으로한 연구가 수행되기를 기대한다.

• 한국위성정보통신학회논문지
• /
• 제9권4호
• /
• pp.91-96
• /
• 2014

본 논문은 적외선 열영상에서 영상의 밝기와 대비 변화에 따라 적응적으로 화염 후보 영역을 검출하기 위한 적응 문턱치를 제안한다. 현장에 사용 되고 있는 화재 검출 시스템은 카메라의 설치 장소에 따라 얻어지는 영상의 밝기나 대비의 변화가 발생 하여 고정된 문턱치를 적용하는 화재 검출 알고리즘의 성능이 변화하게 되므로 환경에 적응적인 문턱치가 필요하다. 제안하는 적응 문턱치를 이용한 화염 검출 알고리즘은 화염의 특성인 온도와 동적임 특성을 분석하여 화염을 검출 한다. 실험을 위해 고정 문턱치를 이용한 화염 검출 알고리즘과 비교 하였으며 제안된 적응 문턱치를 이용한 화염 검출 알고리즘은 화염 검출률 91.42%이며 고정 문턱치를 적용 하였을 때 보다 오검출률을 약 20%가 감소한다. 그리고 영상의 밝기와 대비 변화에 의한 검출 결과가 일정함을 보여 준다.

• 대한원격탐사학회지
• /
• 제19권5호
• /
• pp.403-409
• /
• 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.

• 대한원격탐사학회지
• /
• 제15권3호
• /
• pp.239-251
• /
• 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.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
• /
• pp.149-152
• /
• 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.

• 센서학회지
• /
• 제20권4호
• /
• pp.230-233
• /
• 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
• /
• 제54권4호
• /
• pp.527-544
• /
• 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
• /
• 제13권1호
• /
• pp.67-72
• /
• 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.