• Ceramist
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• v.10 no.3
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• pp.55-66
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• 2007

Infrared Scanning Near-field Optical Microscopy (IR-SNOM) is an extremely powerful analytical instrument since it combines IR spectroscopy's high chemical specificity with SNOM's high spatial resolution. In order to do this in the infrared, specialty chalcogenide glass fibers were fabricated and their ends tapered to generate SNOM probes. The fiber tips were installed in a modified near field microscope and both inorganic and biological samples illuminated with the tunable output from a free-electron laser located at Vanderbilt University. Both topographical and IR spectral images were simultaneously recorded with a resolution of ${\sim}50\;nm$ and ${\sim}100\;nm$, respectively. Unique spectroscopic features were identified in all samples, with spectral images exhibiting resolutions of up to ${\lambda}/60$, or at least 30 times better than the diffraction limited lens-based microscopes. We believe that IR-SNOM can provide a very powerful insight into some of the most important bio-medical research topics.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.29-36
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• 2015

In this paper, we propose the horizontal line detection using the Haar-like features and linear regression in infrared images. In the marine environment horizon image is very useful information on a variety of systems. In the proposed method Haar-like features it was noted that the standard deviation be calculated in real time on a static area. Based on the pixel position, calculating the standard deviation of the around area in real time and, if the reaction is to filter out the largest pixel can get the energy map of the area containing the straight horizontal line. In order to select a horizontal line of pixels from the energy map, we applied the linear regression, calculating a linear fit to the transverse horizontal line across the image to select the candidate optimal horizontal. The proposed method was carried out in a horizontal line detecting real infrared image experiment for day and night, it was confirmed the excellent detection results than the legacy methods.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.313-319
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• 2018

We propose a post-processing algorithm to improve the quality of the RGB image generated by deep learning based colorization from the gray-scale image of an infrared camera. Wavelet fusion is used to generate a new luminance component of the RGB image luminance component from the deep learning model and the luminance component of the infrared camera. PSNR is increased for all experimental images by applying the proposed algorithm to RGB images generated by two deep learning models of SegNet and DCGAN. For the SegNet model, the average PSNR is improved by 1.3906dB at level 1 of the Haar wavelet method. For the DCGAN model, PSNR is improved 0.0759dB on the average at level 5 of the Daubechies wavelet method. It is also confirmed that the edge components are emphasized by the post-processing and the visibility is improved.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1073-1082
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• 2023

Thermal sensors, also called thermal infrared wavelength sensors, measure temperature based on the intensity of infrared signals that reach the sensor. The infrared signals recognized by the sensor include infrared wavelength(0.7~3.0㎛) and radiant infrared wavelength(3.0~100㎛). Infrared(IR) wavelengths are divided into five bands: near infrared(NIR), shortwave infrared(SWIR), midwave infrared(MWIR), longwave infrared(LWIR), and far infrared(FIR). Most thermal sensors use the LWIR to capture images. Thermal sensors measure the temperature of the target in a non-contact manner, and the data can be affected by the sensor's viewing angle between the target and the sensor, the amount of atmospheric water vapor (humidity), air temperature, and ground conditions. In this study, the characteristics of three thermal imaging sensor models that are widely used for observation using unmanned aerial vehicles were evaluated, and the optimal application field was determined.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.523-533
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• 2022

This paper presents a two-stage spatio-spectral fusion method (2SSFM) based on area-to-point regression kriging (ATPRK) to enhance spatial and spectral resolutions using multi-sensor satellite images with complementary spatial and spectral resolutions. 2SSFM combines ATPRK and random forest regression to predict spectral bands at high spatial resolution from multi-sensor satellite images. In the first stage, ATPRK-based spatial down scaling is performed to reduce the differences in spatial resolution between multi-sensor satellite images. In the second stage, regression modeling using random forest is then applied to quantify the relationship of spectral bands between multi-sensor satellite images. The prediction performance of 2SSFM was evaluated through a case study of the generation of red-edge and short-wave infrared bands. The red-edge and short-wave infrared bands of PlanetScope images were predicted from Sentinel-2 images using 2SSFM. From the case study, 2SSFM could generate red-edge and short-wave infrared bands with improved spatial resolution and similar spectral patterns to the actual spectral bands, which confirms the feasibility of 2SSFM for the generation of spectral bands not provided in high spatial resolution satellite images. Thus, 2SSFM can be applied to generate various spectral indices using the predicted spectral bands that are actually unavailable but effective for environmental monitoring.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.387-397
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• 2004

The wavelet transform providing both of the frequency and spatial information of an image is proved to be very much effective for the compression of images, and recently lot of studies on coding algorithms for images decomposed by the wavelet transform together with the multi-resolution theory are going on. This paper proposes a quadtree decomposition method of image compression applied to the images decomposed by wavelet transform by using the correlations between pixels and '0'data grouping. Since the coefficients obtained by the wavelet transform have high correlations between scales and high concentrations, the quadtree method can reduce the data quantity effectively. the experimental infrared image with 256${\times}$256 size and 8〔bit〕, was used to compare the performances of the existing and the proposed compression methods.

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.19-26
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• 2013

The propose of this study is to analyze the optimum spatial resolution of the urban spatial thermal environment structure and to evaluate of the possibility detection using aerial photographs and thermal satellite images. The proper techniques of the optimum spatial resolution for the urban spatial thermal environment structure were analyzed. Thermal infrared satellite image of Seoul city were used for the change rate of surface temperature variation and suggested to the spatial extent and effects of urban surface characteristics and spatial data was interpreted as regions. To extract the surface temperature, Landsat thermal infrared satellite image compared with an automatic weather station data and in the field of the measured temperature and surface temperature by thermal environment affects, the spatial domain has been verified. The surface temperature of the satellite images to extract after adjusting surface temperature isotherms were constructed. The changes in surface temperature from 2008 to 2012 the average surface temperature observation images of changing areas were divided into space. The results of this study are as follows: Through analysis of satellite imagery, Seoul city surface temperature change due to extraction comfort indices were classified into four grades. The comfort index indicative of the temperature of Gangnam-gu, $23.7{\sim}27.2(^{\circ}C)$ range and Songpagu, a $22.7{\sim}30.6(^{\circ}C)$ respectively, the surface temperature of Yeouido $25.8{\sim}32.6(^{\circ}C)$ were in the range.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.203-209
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• 2015

This paper discusses the effectiveness of using Landsat 8 TIR (Thermal Infrared) band images to improve the accuracy of landuse/landcover classification of urban areas. According to classification results for the study area using diverse band combinations, the classification accuracy using an image fusion process in which the TIR band is added to the visible and near infrared band was improved by 4.0%, compared to that using a band combination that does not consider the TIR band. For urban area landuse/landcover classification in particular, the producer’s accuracy and user’s accuracy values were improved by 10.2% and 3.8%, respectively. When MLC (Maximum Likelihood Classification), which is commonly applied to remote sensing images, was used, the TIR band images helped obtain a higher discriminant analysis in landuse/landcover classification.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.4
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• pp.592-604
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• 2019

This study examines the stealth function of sputtered fabric with an infrared thermal imaging camera in terms of the thermal and infrared (IR) transmittance characteristics. Various base fabrics were selected, infrared imaging was performed, and infrared transmittance was measured. By infrared camera experiment it was found that the sample was concealed because it had a similar color to the surroundings when the aluminum layer was directed toward the outside. In addition, a comparison of the infrared thermographic image of the untreated sample and the sputtered sample in the laboratory showed that the difference in ${\Delta}E$ value ranged from 31 to 90.4 and demonstrated effective concealment. However, concealment was not observed in the case of the 3-layer (Nylon-Al-Nylon) model when a sputtered aluminum layer existed between two nylon layers. The direction of the sputtering layer did not affect the infrared transmittance in the infrared transmittance experiment. Therefore, it seems better to interpret the concealing effect in the infrared thermographic images by using thermal transfer theory rather than infrared transmittance theory. We believe that the results of this study will be applicable to developing high performance smart clothing and military uniforms.