• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.709-719
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• 1997

Performance of an algorithm can be mesaurde from serval aspects.Suppose thre is a query formulation al-gorithm.Even though this algorithm shows high retrival performance, ie, high recall and percision, retriveing items can rake a long time.In this study, we time complexity of automatic query reformulation algorithms, named the query Tree, DNF method, and Dillon's method, and comparethem in theoretical and practical aspects using a tral-time performance)the absolute times for each algorithm to fromulate a query)in a Sun SparcStation 2. In experiments using three test sets, CSCM, CISI, and Medlars, the query Tree algorithm was the fastest among the three algorithms tested.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.38-42
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• 2016

In the growing data saturated world, the question of "whether data can be used" has shifted to "can it be utilized effectively?" More data is being generated and utilized than ever before. As the collection of data increases, data mining techniques also must become more and more accurate. Thus, to ensure this data is effectively utilized, the analysis of the data must be efficient. Interpretation of results from the analysis of the data set presented, have their own on the basis it is possible to obtain the desired data. In the data mining method a decision tree, clustering, there is such a relationship has not yet been fully developed algorithm actually still impact of various factors. In this experiment, the classification method of data mining techniques is used with easy decision tree. Also, it is used special technology of one R and J48 classification technique in the decision tree. After selecting a rule that a small error in the "one rule" in one R classification, to create one of the rules of the prediction data, it is simple and accurate classification algorithm. To create a rule for the prediction, we make up a frequency table of each prediction of the goal. This is then displayed by creating rules with one R, state-of-the-art, classification algorithm while creating a simple rule to be interpreted by the researcher. While the following can be correctly classified the pattern specified in the classification J48, using the concept of a simple decision tree information theory for configuring information theory. To compare the one R algorithm, it can be analyzed error rate and accuracy. One R and J48 are generally frequently used two classifications${\ldots}$