• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1309-1320
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• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.33-41
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• 2019

Purpose : To evaluate the effectiveness of Jaw-tracking(JT) technique in Intensity-modulated radiation therapy(IMRT) and Volumetric-modulated arc therapy(VMAT) for radiation therapy of esophageal cancer by analyzing volume dose of perimetrical normal organs along with the low-dose volume regions. Materials and Method: A total of 27 patients were selected who received radiation therapy for esophageal cancer with using $VitalBeam^{TM}$(Varian Medical System, U.S.A) in our hospital. Using Eclipse system(Ver. 13.6 Varian, U.S.A), radiation treatment planning was set up with Jaw-tracking technique(JT) and Non-Jaw-tracking technique(NJT), and was conducted for the patients with T-shaped Planning target volume(PTV), including Supraclavicular lymph nodes(SCL). PTV was classified into whether celiac area was included or not to identify the influence on the radiation field. To compare the treatment plans, Organ at risk(OAR) was defined to bilateral lung, heart, and spinal cord and evaluated for Conformity index(CI) and Homogeneity index(HI). Portal dosimetry was performed to verify a clinical application using Electronic portal imaging device(EPID) and Gamma analysis was performed with establishing thresholds of radiation field as a parameter, with various range of 0 %, 5 %, and 10 %. Results: All treatment plans were established on gamma pass rates of 95 % with 3 mm/3 % criteria. For a threshold of 10 %, both JT and NJT passed with rate of more than 95 % and both gamma passing rate decreased more than 1 % in IMRT as the low dose threshold decreased to 5 % and 0 %. For the case of JT in IMRT on PTV without celiac area, $V_5$ and $V_{10}$ of both lung showed a decrease by respectively 8.5 % and 5.3 % in average and up to 14.7 %. A $D_{mean}$ decreased by $72.3{\pm}51cGy$, while there was an increase in radiation dose reduction in PTV including celiac area. A $D_{mean}$ of heart decreased by $68.9{\pm}38.5cGy$ and that of spinal cord decreased by $39.7{\pm}30cGy$. For the case of JT in VMAT, $V_5$ decreased by 2.5 % in average in lungs, and also a little amount in heart and spinal cord. Radiation dose reduction of JT showed an increase when PTV includes celiac area in VMAT. Conclusion: In the radiation treatment planning for esophageal cancer, IMRT showed a significant decrease in $V_5$, and $V_{10}$ of both lungs when applying JT, and dose reduction was greater when the irradiated area in low-dose field is larger. Therefore, IMRT is more advantageous in applying JT than VMAT for radiation therapy of esophageal cancer and can protect the normal organs from MLC leakage and transmitted doses in low-dose field.