• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.267-275
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• 2015

Those land cover maps have widely been used in various fields, such as environmental studies, military strategies as well as in decision-makings. This study proposes a method to extract training data, automatically and classify the cover using ingle satellite images and national land cover maps, provided by the Ministry of Environment. For this purpose, as the initial training data, those three were used; the unsupervised classification, the ISODATA, and the existing land cover maps. The class was classified and named automatically using the class information in the existing land cover maps to overcome the difficulty in selecting classification by each class and in naming class by the unsupervised classification; so as achieve difficulty in selecting the training data in supervised classification. The extracted initial training data were utilized as the training data of MLC for the land cover classification of target satellite images, which increase the accuracy of unsupervised classification. Finally, the land cover maps could be extracted from updated training data that has been applied by an iterative method. Also, in order to reduce salt and pepper occurring in the pixel classification method, the MRF was applied in each repeated phase to enhance the accuracy of classification. It was verified quantitatively and visually that the proposed method could effectively generate the land cover maps.

• PNF and Movement
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• v.20 no.2
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• pp.243-251
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• 2022

Purpose: This study aimed to compare the effects of aquatic and land dual-task training on balance, gait, and depression in chronic stroke patients. Methods: A total of 24 patients diagnosed with chronic stroke were the subjects. They were assigned to either the experimental group (n = 12) or the control group (n = 12). The experimental group performed aquatic dual-task training, while the control group performed land dual-task training. The aquatic and land dual-task training sessions were conducted once a day for 30 min, 5 days per week, for 6 weeks. Balance was measured using the Berg balance scale. Gait was measured using the Timed Up and Go Test. The Beck's Depression Inventory was used to measure depression. Results: Both the experimental and control groups showed significant differences in balance, gait, and depression after the intervention (p < 0.05) in the within-group comparisons. It was found that the experimental group showed more significant differences in balance, gait, and depression than the control group (p < 0.05) when the two groups were compared. Conclusion: It can be concluded that aquatic dual-task training effectively improved the balance ability, gait ability, and chronic stroke patients' depression based on these results.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1089-1100
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• 2017

This paper presented a method using past land cover maps in image segmentation and training set collection for updating land cover maps. In this method, the object boundaries in past land cover maps were used for segmenting image clearly. Also, the classes of past land cover maps were used to extract additional informative training set from the initial classification result using a small number of initial training set. To evaluate the applicability of proposed method, a case study for updating land cover maps was carried out using middle-level land cover maps and WorldView-2 image in the Taean-gun, South Korea. As a result of the case study, the confusions between urban and barren, paddy/dry field and grassland in the initial classification result were reduced by adding training set. In addition, the object segmentation using boundaries of past land cover map cleared land cover boundaries and improved classification accuracy. Based on the result of case study, the proposed method using past land cover maps is expected to be useful for updating land cover maps.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.674-682
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• 2009

The purpose of this study was to investigate the effect of aquatic gait training on plantar foot pressure, foot kinesiology and gait speed in right hemiplegic patients. The subject were 20 stroke patients who elapsed from 12 month to 24 month after stroke(aquatic gait training group(n=10), land gait training group(n=10)). This study measured plantar foot pressure, toe out angle, subtalar joint angle, gait speed from data of gate on 2m long measuring apparatus for RS-scan system(RS scan Ltd. German). This experiment performed in twice, before and after the aquatic gait training and land gait training. Collected data were statistically analyzed by SPSS Ver. 12.0 using descriptive statistics, paired t-test. Aquatic gait training group had more variety pressure area on their foot such as T1(Toe 1), HM(Heel medial), and HL(Heel lateral). But motion of subtalar joint flexibility and toe out angle decreased considerably and gate speed also increased. According to the result, aquatic gait training is considered as more effective way in foot stability and normal gait pattern than land gait training.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.224-232
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• 2022

The purpose of this study was to investigate the pre- and post-exercise performance, edema, and pain of plyometrics in water and land environments. Twelve males in their 20s were selected as subjects and performed 10 sets of squat jumps 10 times in 2 environmental conditions (water and ground). There was no significant difference in iEMG of vastus medilais according to exercise conditions and time. In MPV of CMJ, there was no significant difference according to exercise conditions and time. The thigh circumference showed a significant difference according to the exercise condition and time, and was higher in the ground condition after exercise. There was a significant difference in pain according to the exercise condition and time, and it was found to be high after exercise, 48 hours, and 72 hours in the ground condition. We believe that plyometric training in an aquatic environment will have less swelling and pain compared to plyometric training conducted in a land environment, and the pain will improve quickly, so we think that training can be conducted in a relatively shorter period than in the land environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.27-37
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• 2002

The classification accuracy of land cover has been considered as one of the major issues to estimate pollution loads generated from diffuse landuse patterns in a watershed. This research aimed to assess the effects of the acquisition methods and sampling size of training reference data on the classification accuracy of land cover using an imagery acquired by optical sensor(OPS) on JERS-1. Two kinds of data acquisition methods were considered to prepare training data. The first was to assign a certain land cover type to a specific pixel based on the researchers subjective discriminating capacity about current land use and the second was attributed to an aerial photograph incorporated with digital maps with GIS. Three different sizes of samples, 0.3%, 0.5%, and 1.0% of all pixels, were applied to examine the consistency of the classified land cover with the training data of corresponding pixels. Maximum likelihood scheme was applied to classify the land use patterns of JERS-1 imagery. Classification run applying an aerial photograph achieved 18 % higher consistency with the training data than the run applying the researchers subjective discriminating capacity. Regarding the sample size, it was proposed that the size of training area should be selected at least over 1% of all of the pixels in the study area in order to obtain the accuracy with 95% for JERS-1 satellite imagery on a typical small-to-medium-size urbanized area.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• 재활복지
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• v.17 no.4
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• pp.383-399
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• 2013

The purpose of this study was to comparison the effects of aqua-and-land based obstacle training on balance of chronic stroke patients. Subjects were randomly divided into an aqua group(n=15) and d land group(n=15). Both group received obstacle training for 40 minutes, 3 times a week during 12 weeks. Static balance was assessed by measuring the mean velocity of mediolateral, anteroposterior and sway area with the eyes open using Good Balance System. Dynamic balance was assessed by measuring Functional Reaching Test(FRT) and the Timed Up and Go test(TUG). Following the intervention, both groups showed significant changes static balance(the mean velocity of mediolateral, anteroposterior and sway area) and dynamic balance(FRT and TUG). There were significant difference in the mean velocity of mediolateral, anteroposterior, sway area, FRT and TUG between the two groups after the interventions. The results of this study suggest that the aqua group and land group were increase balance functions of chronic stroke patients. The aqua group was significantly higher than the land group for patients with chronic stroke patients. We hope that aquatic training can be useful for patients with chronic stroke patients to improve balance functions and the aqua training research for improve balance functions will be conducted continuously.