• 대한공간정보학회지
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• 제10권5호
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• pp.45-51
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• 2002

The goal of image merging techniques are to enhance the resolution of low-resolution images using the detail information of the high-resolution images. Among the several image merging methods, wavelet-based image merging techniques have the advantages of efficient decorrelation of image bands and time-scale analysis. However, they have no regard for spatial information between the bands. In other words, multiresolution data merging methods merge the same information-the detail information of panchromatic image-with other band images, without considering specific characteristics. Therefore, a merged image contains much unnecessary information. In this paper, we discussed this 'mixing' effect and, proposed a method to classify the detail information of the panchromatic image according to the spatial and spectral characteristics, and to minimize distortion of the merged image.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.125-127
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• 2004

High-resolution satellite images offer abundance information of the earth surface for remote sensing applications. The information includes geometry, texture and attribute characteristic. The pixel-based image classification can't satisfy high-resolution satellite image's classification precision and produce large data redundancy. Object-oriented information extraction not only depends on spectrum character, but also use geometry and structure information. It can provide an accessible and truly revolutionary approach. Using Beijing Spot 5 high-resolution image and object-oriented classification with the eCognition software, we accomplish the cultures' precise classification. The test areas have five culture types including water, vegetation, road, building and bare lands. We use nearest neighbor classification and appraise the overall classification accuracy. The average of five species reaches 0.90. All of maximum is 1. The standard deviation is less than 0.11. The overall accuracy can reach $95.47\%.$ This method offers a new technology for high-resolution satellite images' available applications in remote sensing culture classification.

• Nuclear Engineering and Technology
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• 제55권7호
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• pp.2567-2571
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• 2023

Scintillator materials are widely used in the medical and industrial fields for imaging systems using gamma cameras. In this study, image evaluation is performed by modeling a gamma camera system based on a lutetium-yttrium oxyorthosilicate (LYSO) scintillation detector using a pinhole collimator that can improve the spatial resolution. A LYSO detector-based gamma camera system is modeled using a Monte Carlo simulation tool. The geometric concept of the pinhole collimator is designed using various magnification factors, and the spatial resolution is measured using the acquired source image. To evaluate the resolution, the full width at half maximum (FWHM) and natural image quality assessment (NIQE), a no-reference-based parameter, are used. We confirm that the FWHM and NIQE values decrease simultaneously when the diameter of the pinhole collimator increases. Additionally, we confirm that the spatial resolution improves as the magnification factor increases under the same pinhole diameter condition. Particularly, a 0.57 mm FWHM value is obtained using the modeled gamma camera system with a LYSO scintillation detector. In conclusion, our results demonstrate that a pinhole collimator with a LYSO scintillation detector is a promising gamma camera imaging system.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.649-652
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• 2005

In this paper we present a detail approach of the determination of IFOV (Instantaneous Field of View) of high-resolution (l m) panchromatic satellite image over test site. IFOV is the representative measurements as the determination of the spatial resolution in remote sensed imaging system. It can be defined as some area on the ground with the particular altitude when the satellite acquires the image at any given time. Especially, spatial resolution of passive sensors primarily depends on their IFOV. The determination of IFOV goes through simple steps of procedure as followings: Firstly, the GSD (Ground Sample Distance) should be computed at each point on the geometrically corrected image. Then, The GSD is converted into the IFOV. So we are going to explain our test procedures and results.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.221-224
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• 2010

When the image registration methods which were generally used to the low medium resolution satellite images is applied to the high spatial resolution images, some matching errors or limitations might be occurred because of the local distortions in the images. This paper, therefore, proposed the automatic image-to-image registration of high resolution satellite images using local properties of control points to improve the registration result.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.294-303
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• 2021

Various types of sensors are used at industrial sites to measure vibration. With the increase in the diversity of vibration measurement methods, vibration monitoring methods using camera equipment have recently been introduced. However, owing to the physical limitations of the hardware, the measurement resolution is lower than that of conventional sensors, and real-time processing is difficult because of extensive image processing. As a result, most such methods in practice only monitor status trends. To address these disadvantages, a high-resolution vibration measurement method using image analysis of the edge region of the structure has been reported. While this method exhibits higher resolution than the existing vibration measurement technique using a camera, it requires significant amount of computation. In this study, a method is proposed for rapidly processing considerable amount of image data acquired from vision equipment, and measuring the vibration of structures with high resolution. The method is then verified through experiments. It was shown that the proposed method can fast measure vibrations of structures remotely.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.98-101
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• 2019

At present, deep convolutional neural networks have made a very important contribution in single-image super-resolution. Through the learning of the neural networks, the features of input images are transformed and combined to establish a nonlinear mapping of low-resolution images to high-resolution images. Some previous methods are difficult to train and take up a lot of memory. In this paper, we proposed a simple and compact deep recursive residual network learning the features for single image super resolution. Global residual learning and local residual learning are used to reduce the problems of training deep neural networks. And the recursive structure controls the number of parameters to save memory. Experimental results show that the proposed method improved image qualities that occur in previous methods.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• 대한원격탐사학회지
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• 제26권3호
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• pp.317-324
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• 2010

In recent years, many automatic classification approaches have been employed. An automatic classification method can be effective, time-saving and can produce objective results due to the exclusion of operator intervention. This paper proposes a classification method based on automated training for high resolution multispectral images using ancillary data. Generally, it is problematic to automatically classify high resolution images using ancillary data, because of the scale difference between the high resolution image and the ancillary data. In order to overcome this problem, the proposed method utilizes the classification results of a Landsat image as a medium for automatic classification. For the classification of a Landsat image, a maximum likelihood classification is applied to the image, and the attributes of ancillary data are entered as the training data. In the case of a high resolution image, a K-means clustering algorithm, an unsupervised classification, was conducted and the result was compared to the classification results of the Landsat image. Subsequently, the training data of the high resolution image was automatically extracted using regular rules based on a RELATIONAL matrix that shows the relation between the two results. Finally, a high resolution image was classified and updated using the extracted training data. The proposed method was applied to QuickBird and SPOT-5 images of non-accessible areas. The result showed good performance in accuracy assessments. Therefore, we expect that the method can be effectively used to automatically construct thematic maps for non-accessible areas and update areas that do not have any attributes in geographic information system.

• 한국정보통신학회논문지
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• 제25권4호
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• pp.543-548
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• 2021

이미지는 해상도가 높을수록 이미지를 시청하는 사람들의 만족도가 높아지며 초고해상도 이미지화는 컴퓨터 비전이나 영상처리 분야 중에서도 연구 가치가 꽤 높아지고 있다. 본 연구에서는 주로 딥 러닝 초 해상도 모델을 사용하여 저해상도 이미지 LR의 주요 특징을 추출한다. 추출된 특징을 학습 및 재구성하고, 고해상도 이미지 HR을 생성하는 재구성 기반 알고리즘에 중점을 둔다. 본 논문에서는 재구성에 기반을 둔 초 해상도 알고리즘 모델에서 SRCNN과 VDSR에 대하여 알아보도록 한다. SRCNN과 VDSR모델의 구조 및 알고리즘 프로세스를 간략하게 소개하고 개선된 성능평가 함수에서도 다중 채널과 특수한 형태에 대하여 알아보도록 하며, 실험을 통하여 각 알고리즘의 성능을 이해하도록 한다. 실험에서는 SRCNN 및 VDSR 모델의 결과와 피크 신호 대 잡음 비 및 이미지 구조 유사도를 비교하는 실험을 수행하여 결과를 한눈에 볼 수 있도록 하였다.