• Korean Journal of Soil Science and Fertilizer
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• v.34 no.5
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• pp.342-349
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• 2001

This study was carried out to classify and evaluate the land cover map using Landsat TM data in Pyeongtaeg City. DGPS data, aerial photography, topographical map were used for selection the training sets and accuracy assessment. The overall accuracy and Kappa coefficient of the land cover classification map(using supervised classification with 13 classes) with Landsat TM data(16 June. 1997) were respectively, 86.8%, 85.4%, but the user's accuracy of urban/village and vinyl-house was below 60%, and the producer's accuracy of read and vinyl-house below 70%. Maybe it was caused the spectral reflectance characteristics, heterogeneity and small distribution area on the artificial things such as urban/village, vinyl_house and road, etc. And then, the agricultural land cover classification system using remote sensing data in Korea was to classify level I and II. Level I consisted of 5 classes such as agricultural land, forest land, water, barren land, urban and built-up land.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.166-178
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• 2023

This study aims to introduce and assess CNN Deep Learning methods to analyze visual landscape images on social media with embedded user perceptions and experiences. This study analyzed visual landscape images by focusing on a healing place. For the study, seven adjectives related to healing were selected through text mining and consideration of previous studies. Subsequently, 50 evaluators were recruited to build a Deep Learning image. Evaluators were asked to collect three images most suitable for 'healing', 'healing landscape', and 'healing place' on portal sites. The collected images were refined and a data augmentation process was applied to build a CNN model. After that, 15,097 images of 'healing' and 'healing landscape' on portal sites were collected and classified to analyze the visual landscape of a healing place. As a result of the study, 'quiet' was the highest in the category except 'other' and 'indoor' with 2,093 (22%), followed by 'open', 'joyful', 'comfortable', 'clean', 'natural', and 'beautiful'. It was found through research that CNN Deep Learning is an analysis method that can derive results from visual landscape image analysis. It also suggested that it is one way to supplement the existing visual landscape analysis method, and suggests in-depth and diverse visual landscape analysis in the future by establishing a landscape image learning dataset.

• Journal of the Korean Geographical Society
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• v.36 no.4
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• pp.458-473
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• 2001

The classified map of geomorphic surfaces is the most basic data for the geomorphological research. Up to recent days, the traditional methods extracting specific geomorphic surfaces are accomplished by analyzing the aerial photographs and topographical maps, and field works. Also it needs a lot of time and expertness. Furthermore it is difficult to gain the aerial photographs in Korea. Since digital maps in Korean Peninsula are almost completed recently, we tried to extract specific surfaces by analyzing the characteristics of marine terraces based on the level of paleoshoreline and slope analysis on the terrace surface using GIS. However, research used GIS was hardly found up to date, therefore many problems are not be solved yet. The aim of this study is to develop the more efficient and objective method for the extraction and classification of specific geomorphic surfaces by using GIS in Gampo-eup, Gyeongju city, Southeastem Coast in Korea, where a lot of traditional research has already accomplished. For this aim, we have designed the process of extracting specific geomorphic surfaces, chosen the factors that was Gyeongiu city, Southeastem Coast in Korea, where a lot of traditional research has already accomplished. For this aim, we have designed the process of extracting specific geomorphic surfaces, chosen the factors that was suitable for classification of specific geomorphic surface, and presented method of setting up optimum criteria of extraction. As last, effectiveness and problems of these methods were investigated through conincidence rate and error rate.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.255-261
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• 2014

Advances of digital device technology such as digital cameras, smart phones and tablets, provide convenience way for people to take pictures during his/her life. Photo data is being spread rapidly throughout the social network, causing the excessive amount of data available on the internet. Photo retrieval is categorized into three types, which are: keyword-based search, example-based search, visualize query-based search. The commonly used multimedia search methods which are implemented on Smart TV are adapting the previous methods that were optimized for PC environment. That causes some features of the method becoming irrelevant to be implemented on Smart TV. This paper proposes a novel Visual Query-based Photo Retrieval Method in Smart TV Environment using a motion sensing input device known as Kinect Sensor. We detected hand gestures using kinect sensor and used the information to mimic the control function of a mouse. The average precision and recall of the proposed system are 81% and 80%, respectively, with threshold value was set to 0.7.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.109-122
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• 2010

This study is to suggest some ideas for improvements of classification and expansion of the arts in the KDC. In order to this study, analysed changes of terminology, auxiliary tables and notes, and expansion of classification number of the arts from 1st edition to 5th edition of the KDC. The arts of KDC did not changed from 1st to 3rd edition and changed in the 4th edition and 5th edition, and errors and problems of previous edition were not improved, and Classification number and expansion of KDC found out poor rather than different classification schedule because had a lot of Including notes. The result of analysis proposed to improved method to solve the problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.149-156
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• 2006

Existing method of landuse classification using aerial photographs or field survey requires relatively higher amount of time and cost due to necessary manual work. Especially in urban area where the pattern of landuse is densely aggregated, a landuse classification using satellite image is more complex. In this background, this study proposes a landuse classification method to utilize 1:1,000 digital topographic data and IKONOS satellite image. To prove the possibility of this method, the method was applied to Seoul metropolitan area. The results shows the total accuracy of approximately 95% and 14 landuse classes extracted. Based on the results from the pilot study, this method is applicable to landuse classification in urban area.

• Korean Journal of Plant Taxonomy
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• v.43 no.4
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• pp.263-266
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• 2013

Jasminum attenuatum Roxb. ex G. Don, a species of Oleaceae, was recently found in Kon Ka Kinh National Park of Vietnam that represents a new record to the flora of the country. The plant can be differentiated from its allied species J. latipetalum C.B. Clarke and J. simplicifolium subsp. funale (Decne) Kiew by the shape of its leaf blade, in having 5-20 flowered inflorescence, linear bracts, and obconic and glabrous calyx with shorter lobes. The present study provides the species description, illustrations, micrographs by scanning electron microscope of the pollen grains, and important taxonomic notes. Furthermore, a comparison of the diagnostic characters between this species and the above mentioned related species is made.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.447-455
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• 2019

Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.

• Korean Journal of Plant Taxonomy
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• v.42 no.3
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• pp.211-214
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• 2012

Platanthera brevicalcarata Hayata (Orchidaceae), previously known to be distributed from Taiwan to southern Japan, was found in the Jeju-do, Korea. This species is distinguished from the related taxon Platanthera chlorantha by its small plant size, white sepal with distinct one green vein, and short-cylindrical spur. The local name Young-ju-je-bi-ran is originated from the local name of Mt. Halla. The morphological characters and illustration of the species are provided with line drawing and photograph from the natural habitat.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.631-642
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• 2003

We propose an approach to extract and to classify facial features into some classes from one's photo as prepared classification standards to generate one's avatar. Facial Feature Extraction and Classification was executed at eyes, nose, lips, jaw separately and I presented each facial features and classification standards. Extracted Facial Features are used for calculation to features of professional designer's facial component images. Then, most similar facial component images are mapped onto avatar's vector face.