• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.741-749
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• 2022

Major space agencies are planning for the rover-based lunar exploration since water-ice was detected in permanently shadowed regions (PSR). Although sunlight does not directly reach the PSRs, it is expected that reflected sunlight sustains a certain level of low-light environment. In this research, the indoor testbed was made to simulate the PSR's lighting and topological conditions, to which low light enhancement methods (CLAHE, Dehaze, RetinexNet, GLADNet) were applied to restore image brightness and color as well as to investigate their influences on the performance of feature extraction and matching methods (SIFT, SURF, ORB, AKAZE). The experiment results show that GLADNet and Dehaze images in order significantly improve image brightness and color. However, the performance of the feature extraction and matching methods were improved by Dehaze and GLADNet images in order, especially for ORB and AKAZE. Thus, in the lunar exploration, Dehaze is appropriate for building 3D topographic map whereas GLADNet is adequate for geological investigation.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.4
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• pp.84-94
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• 2013

This study scrutinizes the titles of serial poems on Japanese Apricot, which have lucid characters on season and time changes, having been appreciated and recited by the scholars in the Choseon Dynasty era and analyses the records of Zhang zi(1153~1235), a writer in Song(宋) Dynasty in China, having presented the objects harmonizing perfectly with Japanese Apricot. The results of this study categorizes the objects of Japanese Apricot appreciation and establishes the landscape characteristics on Japanese Apricot appreciation affiliated with as follows. First, the objects of Japanese Apricot appreciation are categorized into 'form of blossoms', 'natural feature(景物)', 'place of tree planting', 'the picturesque scene(景色)' and 'behavior'. Second, the scholars regarded the single trees whose branches are grotesque as the objects of appreciation and enjoyed them. They preferred white and single petal Japanese Apricot and admired red Japanese Apricot which has Taoism images. Third, they admired pines and camelias which represent fidelity and strength and valued Japanese Apricot with cranes which remind themselves of solitary scholars. Fourth, they appreciated the images of Japanese Apricot reflected on the water, and the poetically inspiring atmosphere where the trees are planted by the window. Fifth, the moon and snow were crucial weather conditions for appreciating. cold weather and time from night to dawn were ideally suited for enjoying. Sixth, they enjoyed blossoms in various fashions like bottling(甁梅), potting(盆梅), green-housing(龕梅), searching(龕梅) and black-and-white painting(墨梅) with a view to seeing blossoms earlier than the usual flowering time. Moreover, they used paper drapes, bead curtains, mirrors and ice lamps for active appreciation. They also listened to the sound of Piri(wind) and Geomungo(string), played go and drew tea with noble and elegant beauties when they enjoyed Japanese Apricot. The scholars influenced by the neo-Confucianism, which contemplates the objects, attached the specific sentiments like memories, grieves, dreams and farewells to Japanese Apricot and appreciated them. As stated above the scholars enjoyed the landscape including the picturesque scene like climate-weather, time-season and human behaviors not to mention the physical beauty of Japanese Apricot themselves and objects in company with Japanese Apricot including animals and plants.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.