• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.195-200
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• 2008

The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

• 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 Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.61-68
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• 2008

In this paper, we propose a new method to assess fingerprint image quality. In the proposed method, analysis of local variance of image's gray values, local orientation, minutiae density, size and position is applied. Especially by using position information of inputted fingerprint images, partial fingerprint images are filtered and recognition performance is improved. In the experimental results, quality threshold value for improving performance can be decided by analysis of correlation between image quality and recognition rate.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.29-32
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• 2020

Due to the lack of improper image acquisition process, noise induction is an inevitable step. As a result, objective image quality assessment (IQA) plays an important role in estimating the visual quality of noisy image. Plenty of IQA methods have been proposed including traditional signal processing based methods as well as current deep learning based methods where the later one shows promising performance due to their complex representation ability. The deep learning based methods consists of several convolution layers and down sampling layers for feature extraction and fully connected layers for regression. Usually, the down sampling is performed by using max-pooling layer after each convolutional block. We reveal that this max-pooling causes information loss despite of knowing their importance. Consequently, we propose a better IQA method that replaces the max-pooling layers with strided convolutions to down sample the feature space and since the strided convolution layers have learnable parameters, they preserve optimal features and discard redundant information, thereby improve the prediction accuracy. The experimental results verify the effectiveness of the proposed method.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.63-70
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• 2019

These days, as Interest in Image recognition with deep learning is increasing, there has been a lot of research in image recognition using deep learning. In this study, we propose a system for classifying rocks through rock images of 18 types of rock(6 types of igneous, 6 types of metamorphic, 6 types of sedimentary rock) which are addressed in the high school curriculum, using CNN model based on Tensorflow, deep learning open source framework. As a result, we developed a classifier to distinguish rocks by learning the images of rocks and confirmed the classification performance of rock classifier. Finally, through the mobile application implemented, students can use the application as a learning tool in classroom or on-site experience.

• Journal of Environmental Impact Assessment
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• v.3 no.1
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• pp.43-52
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• 1994

This study conducted a visual impact assessment of an apartment complex project proposed in Jungkye-dong, Seoul. Three design alternatives of the project-alternatives 1, 2, and 3-which differed in form, color, scale, and arrangement of buildings were simulated with computer image processing technique. The simulations were presented to the public who were mainly residents in the project area, and visual impact resulted from the alternatives was assessed by them. Their responses were then statistically analyzed. It was found that, in terms of compatibility with the surrounding landscape, alternative 1 was the most favourable because it was more traditional, natural, and diverse than alternatives 2 and 3. At the same time, the alternative was most preferred by the public because it was more plain, natural, and diverse than other alternatives. It was suggested that the visual impact assessment with public participation conducted in this study would help both planners and the public to make more intelligent decisions.

• Proceedings of the Optical Society of Korea Conference
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• 1989.06a
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• pp.100-104
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• 1989

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.3
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• pp.1-11
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• 2015

This study comparatively analyzed the images and preference of lighting space using the emotion-based technique in order to effectively use it in clothing shops and fashion marketing. In terms of color temperature for light sources, 2,800K of lamp color, 6,500K of daylight color and 4,200K of white color were used. For the assessment, sensory evaluation technique was used. Then, the study found the followings: In terms of the image of lighting space by light source, different images were observed by light source with significant difference by the evaluation category. For factor analysis by the evaluation category, 7 factors were extracted. Among them, evaluation on lighting space was influenced by the following three images: modern space, elegant space and classical space. In particular, the modern space comprised of the following adjectives had the biggest effect on the assessment of the image of lighting space ('refreshing,' 'transparent,' 'bluish,' 'bright' and 'non-classical') (primary evaluation 30.13%). According to assessment on the preference of lighting space, the respondents' most favorite lighting space was 4,200K while their least favorable one was 6,500K in terms of color temperature. In terms of preference by the image of lighting space, they didn't like 'non-elegant' and 'non-beige' images even though they had the images of modern space. Therefore, it was confirmed that beige and elegant space images have an effect on the preference of lighting space.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.13-20
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• 2012

Despite of growing concern for developing meteorological satellites, poor investment has been realized to acquire effective and efficient satellite image processing technologies. In this study, not only qualitative exploration on mapping each elementary technology into related industries but quantitative inter-industry analysis induced from Total Cost of Ownership (TCO) of the Korean satellite image processing system were performed. Furthermore, economic assessment has been made by estimating internal rate of return(IRR) for the benefits returned versus TCO of the system. The results showed that agriculture and fisheries industry, tourist and leisure industry, and transportation industry were highly related with the acquisition of the system, and that 9.1 billion won of production-induced effects, 3.3 billion won of value-added-induced effects, and 54 individuals of employment-induced effects were anticipated except for those of directly relevant industries. Even in the pessimistic scenario, 7% of IRR exceeding 5.5% assumed as current public rate was postulated, consequently, the investment was fairly justified.

• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).