• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.5
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• pp.866-882
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• 2024

Regarding the evolving technological landscapes and the integration of fashion imagery into virtual spaces, like the metaverse, this research navigated the transformative dimensions of body expression in contemporary fashion. It is rooted in a posthuman framework, interpreting the novel forms of representation emerging in this dynamic technology-fashion intersection. A comprehensive exploration of posthumanism, transhumanism, and posthuman perspectives preceded the image analysis to elucidate shifting paradigms in portraying the human form within artworks. The analysis of posthuman expressions in fashion imagery unveiled a rejection of hierarchical viewpoints within the human species, challenged gender norms, and embraced diverse body representations. Four distinctive classifications emerged: "Incapacitating of Male Power from Gaze," "Revealing Fearlessly," "Fashioning Non-human Beings," and "Techno-morphism of Body." These categories emphasized transcending conventional beauty standards, highlighting inclusivity by embracing diverse body representations and technologically integrated expressions in fashion. This study addressed the need for more comprehensive ones on evolving posthuman classifications in fashion imagery. The identified trends offered valuable insights for image creators and scholars, encouraging deeper exploration of expressive intentions and perspectives.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.703-715
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• 2011

Forests have been considered one of the most important ecosystems on the earth, affecting the lives and environment. The sustainable forest management requires accurate and timely information of forest and tree parameters. Appropriately interpreted remotely sensed imagery can provide quantitative data for deriving forest information temporally and spatially. Especially, analysis of individual tree detection and crown delineation is significant issue, because individual trees are basic units for forest management. Individual trees in aerial imagery have reflectance characteristics according to tree species, crown shape and hierarchical status. This study suggested a method that identified individual trees and delineated crown boundaries through adopting gradient method algorithm to amplified greenness data using red and green band of aerial imagery. The amplification of specific band value improved possibility of detecting individual trees, and gradient method algorithm was performed to apply to identify individual tree tops. Additionally, tree crown boundaries were explored using spectral intensity pattern created by geometric characteristic of tree crown shape. Finally, accuracy of result derived from this method was evaluated by comparing with the reference data about individual tree location, number and crown boundary acquired by visual interpretation. The accuracy ($\hat{K}$) of suggested method to identify individual trees was 0.89 and adequate window size for delineating crown boundaries was $19{\times}19$ window size (maximum crown size: 9.4m) with accuracy ($\hat{K}$) at 0.80.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.67-73
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• 2005

The urbanization of Korea has been rapidly progressed since 1960, but satellite imagery have provided the information only after 1975. Recently released CORONA imagery is one of the few source of satellite image which can provide 1960's topographic information of the Korean Peninsular. It can be applied to change detection in various fields such as urban, forest, and environmental planning. In this research mosaic image of past Korean Peninsular using CORONA imagery in the 1960s were generated. A polynomial equation and a modified collinearity equation were applied for geo-referencing and a comparative analysis was conducted. In this research the 2nd polynomial equations were used for geo-referencing of CORONA imagery. After carrying out geo-referencing, mosaic image was generated using Erdas Imagine. It is assumed that this result image is very useful for various fields such as generation of thematic maps, urban planning, and change detection.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1474-1474
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• 2003

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.9-17
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• 2017

Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.61-72
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• 2003

Recently, demands with practical applications using high resolution imagery are increasing, according to addressing new sensor data. Since late 1990s, attempts for application to transportation problems of satellite imagery data have been intensively carried out in US, and these kinds of studies are being categorized into the name of RS-T(remote sensing in transportation). Further, this study is also linked with GIS-T(GIS for transportation), being in the matured stage, and then it contributes to wide uses of remotely sensed imagery. In this study, RS-T is briefly summarized. Later, in order to apply urban transportation analysis with satellite imagery as ancillary data, implementation, as prototyped extension program, for extraction of gravity-typed accessibility indices of transportation geography is performed in the ArcView-GIS environment. It is thought that applied results by two models among implemented models in this study can be utilized to characterize transportation accessibility in a region and to apply as useful statistics related to urban transportation status for regional transportation planning, if time series data are used.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.261-274
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• 2004

The feature extraction using remotely sensed imagery has been recognized one of the important tasks in remote sensing applications. As the high-resolution imagery are widely used to the engineering purposes, need of more accurate feature information also is increasing. Especially, in case of the automatic extraction of linear feature such as road using mid or low-resolution imagery, several techniques was developed and applied in the mean time. But quantitatively comparative analysis of techniques and case studies for high-resolution imagery is rare. In this study, we implemented a computer program to perform and compare GDPA (Gradient Direction Profile Analysis) algorithm and Hough transformation. Also the results of applying two techniques to some images were compared with road centerline layers and boundary layers of digital map and presented. For quantitative comparison, the ranking method using commission error and omission error was used. As results, Hough transform had high accuracy over 20% on the average. As for execution speed, GDPA shows main advantage over Hough transform. But the accuracy was not remarkable difference between GDPA and Hough transform, when the noise removal was app]ied to the result of GDPA. In conclusion, it is expected that GDPA have more advantage than Hough transform in the application side.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.89-104
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• 2015

In order to effectively manage forested areas in South Korea on a national scale, using remotely sensed data is considered most suitable. In this study, utilizing Land coverage maps and Forest type maps of national geographic information instead of collecting field data was tested for conducting supervised classification on SPOT-5 and KOMPSAT-2 imagery focusing on forested areas. Supervised classification were conducted in two ways: analysing a whole area around the study site and/or only forested areas around the study site, using Support Vector Machine. The overall accuracy for the classification on the whole area ranged from 54.9% to 68.9% with kappa coefficients of over 0.4, which meant the supervised classification was in general considered moderate because of sub-classifying forested areas into three categories (i.e. hardwood, conifer, mixed forests). Compared to this, the overall accuracy for forested areas were better for sub-classification of forested areas probably due to less distraction in the classification. To further improve the overall accuracy, it is needed to gain individual imagery rather than mosaic imagery to use more spetral bands and select more suitable conditions such as seasonal timing. It is also necessary to obtain precise and accurate training data for sub-classifying forested areas. This new approach can be considered as a basis of developing an excellent analysis manner for understanding and managing forest landscape.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.347-359
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• 2010

Ortho-photos provide valuable spatial and spectral information for various Geographic Information System (GIS) and mapping applications. The absence of relief displacement and the uniform scale in ortho-photos enable interested users to measure distances, compute areas, derive geographic locations, and quantify changes. Differential rectification has traditionally been used for ortho-photo generation. However, differential rectification produces serious problems (in the form of ghost images) when dealing with large scale imagery over urban areas. To avoid these artifacts, true ortho-photo generation techniques have been devised to remove ghost images through visibility analysis and occlusion detection. So far, the Z-buffer method has been one of the most popular methods for true ortho-photo generation. However, it is quite sensitive to the relationship between the cell size of the Digital Surface Model (DSM) and the Ground Sampling Distance (GSD) of the imaging sensor. Another critical issue of true ortho-photo generation using high resolution satellite imagery is the scan line search. In other words, the perspective center corresponding to each ground point should be identified since we are dealing with a line camera. This paper introduces alternative methodology for true ortho-photo generation that circumvents the drawbacks of the Z-buffer technique and the existing scan line search methods. The experiments using real data are carried out while comparing the performance of the proposed and the existing methods through qualitative and quantitative evaluations and computational efficiency. The experimental analysis proved that the proposed method provided the best success ratio of the occlusion detection and had reasonable processing time compared to all other true ortho-photo generation methods tested in this paper.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.163-169
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• 2003