• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.200-221
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• 2019

The National Hangeul Museum houses a document list of the wedding gifts that Queen Sunwon granted her royal son-in-law, Nam Nyeong-wie, in August 1837. A total of 55 items were recorded in Hangeul Gungseo style on yellow paper. This list included four types of government officials' uniforms, casual wear, accessories, etc. The results of studying the 19th-century male officials' uniforms through this historic record were as follows. The clothing record in the List of Wedding Gifts was more accurate than that in the Dukongongju Garyedeungrok. The four types of government official uniforms for Nam Nyeong-wie included jeobok, sangbok, sibok, and gongbok. For the joebok, there was geumkwan, daehong-hangrah joebok, baeksam, ahole, seodae, gakpae, peseul, paeok, husu, and mokhwa. The record of the List confirms that baeksam was used as jungdan for joebok. For the gongbok, there was daehong-hangrah gongbok, blue seolhancho duhgre, blue saenggyeonggwangju changui, and green waehangrah kurimae. The record of the List shows that the lining of the gongbok was duhgre, and confirms, for the first time, that changui and kurimae were worn inside the gongbok. For the sangbok, there was samo, ssanghakhyungbae embroidered with golden thread, dark green cloud patterned gapsa gwandae, blue cloud patterned gapsa duhgre, blue ssangmuncho changui, and Chinese purple hwamunsa kurimae. For the sibok, there was pink gwangsa gwandae, and blue gwangcho duhgre. For the lining, blue duhgre was used. Lastly, the fabrics used for those costumes were all suitable for the climate in August.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.689-698
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• 2017

In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.