• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.637-654
• /
• 2023

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1353-1369
• /
• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1413-1425
• /
• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1097-1109
• /
• 2023

Urban is an area where small-scale changes to individual buildings occur frequently. An existing urban building database requires periodic updating to increase its usability. However, there are limitations in data collection for building changes over a wide urban. In this study, we check the possibility of detecting building changes and updating a building database by using satellite images that can capture a wide urban region by a single image. For this purpose, building areas in a satellite image are first extracted by projecting 3D coordinates of building corners available in a building database onto the image. Building areas are then divided into roof and facade areas. By comparing textures of the roof areas projected, building changes such as height change or building removal can be detected. New height values are estimated by adjusting building heights until projected roofs align to actual roofs observed in the image. If the projected image appeared in the image while no building is observed, it corresponds to a demolished building. By checking buildings in the original image whose roofs and facades areas are not projected, new buildings are identified. Based on these results, the building database is updated by the three categories of height update, building deletion, or new building creation. This method was tested with a KOMPSAT-3A image over Incheon Metropolitan City and Incheon building database available in public. Building change detection and building database update was carried out. Updated building corners were then projected to another KOMPSAT-3 image. It was confirmed that building areas projected by updated building information agreed with actual buildings in the image very well. Through this study, the possibility of semi-automatic building change detection and building database update based on single satellite image was confirmed. In the future, follow-up research is needed on technology to enhance computational automation of the proposed method.

• Journal of Food Hygiene and Safety
• /
• v.38 no.5
• /
• pp.361-372
• /
• 2023

Organotin pesticide is used as an acaricide in agriculture and may contaminate livestock products. This study aims to develop a rapid and straightforward analytical method for detecting organotin pesticides, specifically azocyclotin, cyhexatin, and fenbutatin oxide, in various livestock products, including beef, pork, chicken, egg, and milk, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The extraction process involved the use of 1% acetic acid in a mixture of acetonitrile and ethyl acetate (1:1). This was followed by the addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium chloride. The extracts were subsequently purified using octadecyl (C₁₈) and primary secondary amine (PSA), after which the supernatant was evaporated. Organotin pesticide recovery ranged from 75.7 to 115.3%, with a coefficient of variation (CV) below 25.3%. The results meet the criteria range of the Codex guidelines (CODEX CAC/GL 40). The analytical method in this study will be invaluable for the analysis of organotin pesticides in livestock products.

• Pediatric Infection and Vaccine
• /
• v.31 no.1
• /
• pp.37-45
• /
• 2024

Purpose: This study aimed to examine the clinical features and determinants of macrolide-unresponsive Mycoplasma pneumoniae pneumonia (MUMP) and to assess the differences in the time to fever resolution between doxycycline (DXC), tosufloxacin (TFX) and corticosteroid (CST) as second-line treatment. Methods: We retrospectively analyzed the medical records of patients under the age of 18 who were admitted to Nowon Eulji University Hospital between July 2018 and February 2020, diagnosed with mycoplasma pneumonia. Macrolide resistance was confirmed by detecting point mutations in the 23S rRNA gene. MUMP was clinically defined by persistent fever (≥38.0℃) lasting for 72 hours or more after the initiation of macrolide treatment. In cases of MUMP, patients were treated with an addition of CST, or the initial macrolide was replaced either DXC or TFX. Results: Out of 157 cases of mycoplasma pneumonia, 83 cases (52.9%) did not respond to macrolides. Patients with MUMP exhibited significantly higher C-reactive protein (CRP) levels (3.2±3.0 vs. 2.4±2.2 mg/dL, P=0.047), more frequent lobar/segmental infiltrations or pleural effusions (56.6% vs. 27.0%, P<0.001; 6.0% vs. 0.0%, P=0.032), and a higher prevalence of 23S rRNA gene mutations (96.4% vs. 64.6%, P<0.001) when compared to those with macrolide-susceptible M. pneumoniae pneumonia. In terms of second-line treatment, 15 patients (18.1%) responded to CST, 30 (36.1%) to DXC, and 38 (45.8%) to TFX. The time to defervescence (TTD) after initiation second-line treatment was significantly shorter in the CST group compared to the DXC (10.3±12.7 vs. 19.4±17.2 hours, P=0.003) and TFX groups (10.3±12.7 vs. 25.0±20.1 hours, P=0.043), with no significant difference observed between the DXC and TFX groups (19.4±17.2 vs. 25.0±20.1 hours, P=0.262). Conclusions: High CRP levels, the presence of positive 23S rRNA gene mutation, lobar or segmental lung infiltration, and pleural effusion observed in chest X-ray findings were significant factors associated with macrolide unresponsiveness. In this study, CST demonstrated a shorter TTD compared to DXC or TFX. Further, larger-scale prospective studies are needed to determine the optimal second-line treatment for MUMP.

• Journal of the Korean Society of Radiology
• /
• v.81 no.2
• /
• pp.365-378
• /
• 2020

Purpose This study was performed to determine whether the T1 relaxation time of gadoxetic acid-enhanced liver MR imaging is useful for detecting and staging liver fibrosis in patients with chronic liver disease. Materials and Methods One hundred and three patients with suspected focal liver lesion underwent MR imaging and Fibroscan. Fibroscan was chosen as the reference standard for classifying liver fibrosis. T1 relaxation times were acquired before (preT1), 20 minutes after (postT1) contrast administration, and reduction rate of T1 relaxation time (rrT1) on transverse 3D VIBE (volumetric interpolated breath-hold examination) sequence using 3T MR imaging. The optimal cut-off values for the fibrosis staging were determined with ROC analysis. Results PreT1 and postT1 increased and rrT1 decreased constantly with increasing severity of liver fibrosis according to the METAVIR score (F0-F4). There were statistically significant differences between F2 and F3 in preT1 (F2, 836.0 ± 74.7 ms; F3, 888.6 ± 77.5 ms, p < 0.05) and between F3 and F4 in postT1 (F3, 309.0 ± 80.2 ms; F4, 406.6 ± 147.7 ms, p < 0.05) and rrT1 (F3, 65.4 ± 7.7%; F4, 57.3 ± 11.4%, p < 0.05). ROC analysis revealed that combination test (preT1 + postT1) was the best test for predicting liver fibrosis. Conclusion PreT1 and postT1 increased constantly with increasing severity of liver fibrosis. T1 mapping in gadoxetic acid-enhanced liver MR imaging could be a helpful complementary sequence to determine the liver fibrosis stage.

• Journal of the Korean Society of Radiology
• /
• v.82 no.5
• /
• pp.1196-1206
• /
• 2021

Purpose To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30-50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learning-based AI algorithm for CDSS.

• Journal of the Korean Society of Radiology
• /
• v.82 no.6
• /
• pp.1493-1504
• /
• 2021

Purpose This study aimed to evaluate the utility of the 16-cm axial volume scan technique for calculating the coronary artery calcium score (CACS) using non-enhanced chest CT. Materials and Methods This study prospectively enrolled 20 participants who underwent both, non-enhanced chest CT (16-cm-coverage axial volume scan technique) and calcium-score CT, with the same parameters, differing only in slice thickness (in non-enhanced chest CT = 0.625, 1.25, 2.5 mm; in calcium score CT = 2.5 mm). The CACS was calculated using the conventional Agatston method. The difference between the CACS obtained from the two CT scans was compared, and the degree of agreement for the clinical significance of the CACS was confirmed through sectional analysis. Each calcified lesion was classified by location and size, and a one-to-one comparison of non-contrast-enhanced chest CT and calcium score CT was performed. Results The correlation coefficients of the CACS obtained from the two CT scans for slice thickness of 2.5, 1.25, and 0.625 mm were 0.9850, 0.9688, and 0.9834, respectively. The mean differences between the CACS were -21.4% at 0.625 mm, -39.4% at 1.25 mm, and -76.2% at 2.5 mm slice thicknesses. Sectional analysis revealed that 16 (80%), 16 (80%), and 13 (65%) patients showed agreement for the degree of coronary artery disease at each slice interval, respectively. Inter-reader agreement was high for each slice interval. The 0.625 mm CT showed the highest sensitivity for detecting calcified lesions. Conclusion The values in the non-contrast-enhanced chest CT, using the 16-cm axial volume scan technique, were similar to those obtained using the CACS in the calcium score CT, at 0.625 mm slice thickness without electrocardiogram gating. This can ultimately help predict cardiovascular risk without additional radiation exposure.