• Journal of Korean Society on Water Environment
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• v.30 no.3
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• pp.329-339
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• 2014

This study aims to estimate chlorophyll-a concentration in rivers using multi-spectral RapidEye imagery and Spectral Mixture Analysis (SMA) and assess the applicability of SMA for multi-temporal imagery analysis. Comparison between images (acquired on Oct. and Nov., 2013) predicted and ground reference chlorophyll-a concentration showed significant performance statistically with determination coefficients of 0.49 and 0.51, respectively. Two band (Red-RE) model for the October and November 2013 RapidEye images showed low performance with coefficient of determinations ($R^2$) of 0.26 and 0.16, respectively. Also Three band (Red-RE-NIR) model showed different performance with $R^2$ of 0.016 and 0.304, respectively. SMA derived Chlorophyll-a concentrations showed relatively higher accuracy than band ratio models based values. SMA was the most appropriate method to calculate Chlorophyll-a concentration using images which were acquired on period of low Chlorophyll-a concentrations. The results of SMA for multi-temporal imagery showed low performance because of the spatio-temporal variation of each end members. This approach provides the potential of providing a cost effective method of monitoring river water quality and management using multi-spectral imagery. In addition, the calculated Chlorophyll-a concentrations using multi-spectral RapidEye imagery can be applied to water quality modeling, enhancing the predicting accuracy.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1194-1202
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• 2012

Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields ($3,521km^2$) were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.1-14
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• 2012

A river is defined as the watercourse flowing through its channel, and the mapping tasks of a river plays an important role for the research on the topographic changes in the riparian zones and the research on the monitoring of flooding in its floodplain. However, the utilization of the ground surveying technologies is not efficient for the mapping tasks of a river due to the irregular surfaces of the riparian zones and the dynamic changes of water level of a river. Recently, the spatial information data sets are widely used for the coastal mapping tasks due to the acquisition of the topographic information without human accessibility. In this research, we tried to extract a river from the RapidEye imagery by using the ISODATA(Iterative Self_Organizing Data Analysis) classification algorithm with the two different parameters(NIR (Near Infra-Red) band and NDVI(Normalized Difference Vegetation Index)). First, the two different images(the NIR band image and the NDVI image) were generated from the RapidEye imagery. Second, the ISODATA algorithm were applied to each image and each river was generated in each image through the post-processing steps. River boundaries were also extracted from each classified image using the Sobel edge detection algorithm. Ground truths determined by the experienced expert are used for the assessment of the accuracy of an each generated river. Statistical results show that the extracted river using the NIR band has higher accuracies than the extracted river using the NDVI.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.149-155
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• 2011

A fast-changing agriculture environment induced by global warming and abnormal climate conditions demands scientific systems for monitoring and predicting crop conditions as well as crop yields at national level. Remote sensing opens up a new application field for precision agriculture with the help of commercial use of high resolution optical as well as radar satellite data. In this study, we investigated the multi-temporal spectral characteristics relative to different agricultural land use types in Korea using RapidEye satellite imagery. There were explicit differences between vegetation and non-vegetation land use types. Also, within the vegetation group spectral vegetation indices represented differences in temporal changing trends as to plant species and paddy types.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.722-730
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• 2012

Recently, eye image information has been widely used for iris recognition or gaze detection in biometrics or human computer interaction. According as long distance camera-based system is increasing for user's convenience, the noises such as eyebrow, forehead and skin areas which can degrade the accuracy of eye detection are included in the captured image. And fast processing speed is also required in this system in addition to the high accuracy of eye detection. So, we compared the most widely used algorithms for eye detection such as AdaBoost eye detection algorithm, adaptive template matching+AdaBoost algorithm, CAMShift+AdaBoost algorithm and rapid eye detection method. And these methods were compared with images including light changes, naive eye and the cases wearing contact lens or eyeglasses in terms of accuracy and processing speed.

• KCID journal
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• v.21 no.1
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• pp.45-54
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• 2014

In this study, Agricultural Infrastructures of Shincheon-gun in North Korea are investigated using Kompsat-2 and RapidEye satellite imagery. Target agricultural infrastructures are agricultural landuse, irrigation and drainage canals, dammed pools for irrigation and pumping stations. KOMPSAT-2 satellite imagery are use to investigate agricultural hydraulic structures and agricultural landuse are investigated by RapidEye Imagery. Geometric correction are performed using 28 GCP and QUAC method are used for atmospherical correction in all imagery. ISODATA clustering and naked-eye classification method are used for extracting agricultural hydraulic structures and Object-based analysis is applied to classifying the agricultural landuse. Extraction results of agricultural hydraulic structures and agricultural are presented and we suggest the applicability of satellite imagery to investigate agricultural infrastructures in North Korea.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.823-833
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• 2020

In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.201-202
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• 1998

In this paper we suggested an automated method for detecting and counting rapid eye movement(REM) using EOG during sleep. This method is formulated by two step fuzzy logic. At first step, the velocity and the distance of single channel eye movement are used for the fuzzy input to get the possibility of being REM at each EOG. At second step, the two possibility values of both EOG from the first step and the correlation coefficient of both eye movements are used for the fuzzy logic input, and the output is the final possibility of being Rapid Eye Movement. We applied this algorithm to the normal and narcoleptic sleep data and compared the difference. We found the possibility that the count of REM can be a parameter that has significant physiological meanings.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.47-53
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• 2012

Recently many developed countries have used satellite images for classifying cropland areas to reduce time and efforts put into field survey. Korea also has used satellite images for the same purpose since KOMPSAT-2 was successfully launched and operated in 2006, but still far way to go in order to achieve the required accuracy from the products. This study evaluated the accuracy of the calculated croplands by using the objected classification method with various satellite images including ASTER, Spot-5, Rapid eye, Quickbird-2, Geo eye-1. Also, their usability and effectiveness for the cropland survey were verified by comparing with field survey data. As results. Geo eye-1 and Rapid eye showed higher accuracy to calculate the paddy field areas while Geo eye-1 and Quickbird-2 showed higher accuracy to calculate the upland field areas.

• Annals of Clinical Neurophysiology
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• v.21 no.2
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• pp.94-97
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• 2019

The patients with myotonic dystrophy (MD) show ocular motor abnormalities including strabismus, vergence deficits, and inaccurate or slow saccades. Two theories have been proposed to explain the oculomotor deficits in MD. The central theory attributes the defects of eye movements of MD to the involvement of the central nervous system while the muscular theory attributes to dystrophic changes of the extraocular muscles. A 58-year-old woman with MD showed selective slowing of horizontal saccades and reduced peak velocities for both horizontal canals in head impulse tests, while smooth-pursuit eye movements and vertical head impulse responses were normal. This case suggests that the extraocular muscles-as a final common pathway of the voluntary saccade and reflexive vestibular eye movements-may better explain the defective rapid eye movements observed in MD.