• Journal of The Korean Astronomical Society
• /
• v.38 no.4
• /
• pp.385-414
• /
• 2005

We have searched for infrared emission from supernova remnants (SNRs) included in the Spitzer Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE) field. At the positions of 100 known SNRs, we made 3.6, 4.5, 5.8, and $8.0{\mu}m$ band images covering the radio continuum emitting area of each remnant. In-depth examinations of four band images based on the radio continuum images of SNRs result in the identification of sixteen infrared SNRs in the GLIMPSE field. Eight SNRs show distinct infrared emission in nearly all the four bands, and the other eight SNRs are visible in more than one band. We present four band images for all identified SNRs, and RGB-color images for the first eight SNRs. These images are the first high resolution (<2') images with comparative resolution of the radio continuum for SNRs detected in the mid-infrared region. The images typically show filamentary emission along the radio enhanced SNR boundaries. Most SNRs are well identified in the 4.5 and $5.8{\mu}m$ bands. We give a brief description of the infrared features of the identified SNRs.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.1
• /
• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.5
• /
• pp.16-23
• /
• 2011

Muzzle flash based on gunnery is the target that has huge energy. So, gunnery target in a long range over xx km is distinguishable in the IR(infrared) images, on the other hand, is not distinguishable in the CCD images. In this paper, we propose the classification method of gunnery targets in a infrared images and in a long range. The energy from gunnery have an effect on varous pixel values in infrared images as a property of infrared image sensor, distance, and atmosphere, etc. For this reason, it is difficult to classify gunnery targets using pixel values in infrared images. In proposed method, we take the profile of pixel values using high performance infrared sensor, and classify gunnery targets using modeling GMM and shape of profile. we experiment on the proposed method with infrared images in the ground and aviation. In experimental result, the proposed method provides about 93% classification rate.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.6
• /
• pp.73-80
• /
• 2019

In this paper, we propose an effective dynamic range compression (DRC) method of infrared images. A histogram of infrared images has narrow dynamic range compared to visible images. Hence, it is important to apply the effective DRC algorithm for high performance of an infrared image analysis. The proposed algorithm for high dynamic range divides an infrared image into the overlapped blocks and calculates Shannon's entropy of overlapped blocks. After that, we classify each block according to the value of entropy and apply adaptive histogram modification method each overlapped block. We make an intensity mapping function through result of the adaptive histogram modification method which is using standard-deviation and maximum value of histogram of classified blocks. Lastly, in order to reduce block artifact, we apply hanning window to the overlapped blocks. In experimental result, the proposed method showed better performance of dynamic range compression compared to previous algorithms.

• Transactions on Electrical and Electronic Materials
• /
• v.14 no.4
• /
• pp.187-192
• /
• 2013

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.11a
• /
• pp.143-152
• /
• 2004

Researchers seeking geological and environmental information, depend on remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, adverse weather conditions as well as equipment expense limit the ability to collect data anywhere and anytime. To allow for better flexibility in geological and environmental data collection, we have developed a compact, multi-spectral automatic Aerial Photographic system (PKNU2). This system's Multi-spectral camera can record visible (RGB) and infrared (NIR) band (3032＊2008 Pixels) images Visible and infrared band images were obtained from each camera respectively and produced color-infrared composite images to be analyzed for the purpose of the environmental monitoring. However this did not provide quality data. Furthermore, it has the disadvantage of having the stereoscopic overlap area being 60% unsatisfied due to the 12 seconds of storage time of each data The PKNU2 system in contrast, photographed photos of great capacity Thus, with such results, we have been proceeding to develop the advanced PKNU2 (PKNU3) system that consists of a color-infrared spectral camera that can photograph in the visible and near-infrared bands simultaneously using a single sensor, a thermal infrared camera, two 40G computers to store images, and an MPEG board that can compress and transfer data to the computer in real time as well as be able to be mounted onto a helicopter platform.

• The Bulletin of The Korean Astronomical Society
• /
• v.35 no.1
• /
• pp.70.2-70.2
• /
• 2010

We present near- and mid-infrared images of the Crab Nebula, taken with the Infrared Camera (IRC) onboard the AKARI infrared space telescope. These images have a field-of-view of 10'*10' and show the full extent of the nebula at 3, 4, 7, 11, 15, and 24 um. The Crab nebula in near infrared is dominated by synchrotron emission while, in mid infrared, the ionic forbidden lines of Ar, Ne, S, and Fe makes significant contribution. We separate the line emission from synchrotron emission in 3-15 um AKARI bands using the ISOCAM CVF data, and present separate images for the line and synchrotron emissions in each band. We derive the total synchrotron fluxes of the Crab nebula in these bands, which are used to complete the synchrotron spectral energy distribution of the Crab nebula from radio to X-rays. We discuss the spectral variations of the Crab nebula.

• Journal of the Semiconductor & Display Technology
• /
• v.15 no.3
• /
• pp.51-56
• /
• 2016

This paper proposes a new image enhancement method for an infrared thermal image. The proposed method uses both Laplacian and Prewitt edge detectors. Without a visible light, it uses an infrared image for the edge detection. The method subtracts contour images from the infrared thermal image. It results black contours of objects in the infrared thermal image. That makes the objects in the infrared thermal image distinguished clearly. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using various infrared thermal images. The results show that the proposed method is successful for image enhancement of an infrared thermal image.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1183-1195
• /
• 2020

Visual saliency detection is an essential task because it is an important part of various vision-based applications. There are many techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is limited. In this paper, we introduce a simple approach for saliency detection in infrared images based on the thresholding technique. The input image is thresholded into several Boolean maps, and an initial saliency map is calculated as a weighted sum of the created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and a Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method has high performance when applied to real-life data.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.11
• /
• pp.115-123
• /
• 2015

The infrared light is known to be less dependent on background light compared to the visible light, and thus many applications such as remote sensing and image surveillance use the infrared image. Similar to color images, infrared images can also be degraded by hazy weather condition, and consequently the performance of the infrared image-based applications can decrease. Nevertheless, infrared image dehazing has not received significant interest. In this paper, we analyze the characteristic of infrared images, especially near-infrared (NIR) images, and present an NIR dehazing algorithm using the analyzed characteristics. In particular, a machine learning framework is adopted to obtain an accurate transmission map and several post-processing methods are used for further refinement. Experimental results show that the proposed NIR dehazing algorithm outperforms the conventional color image dehazing method for NIR image dehazing.