• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.1-5
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• 2015

With the help of fast growing multimedia technology and high preference for users of large screens, the newest video coding standard, HEVC (High Efficiency Video Coding) high-quality video compression), has been introduced. Therefore, the high definition image services which are four times more clear than conventional HD video, are getting popular. JPEG 2000 also has stated to support 4K and 8K UHD. As a result, it requires fast processing technology to read and write UHD images. This paper introduces a study on fast parallel processing technology for UHD images. For this purpose, first, JPEG 2000 is reviewed and a GPU based parallel implementation is proposed for a preprocessing of color conversion stage. The parallelled algorithm is implemented with OpenCL (Open Computing Language). The simulation results show that the proposed method shows 5 times performance improvements on processing speed for 4K UHD over the method using threads.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.144-158
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• 2012

A higher spatial resolution is preferable to support the accuracy of detailed climate analysis in urban areas. Airborne LiDAR (Light Detection And Ranging) and satellite (KOMPSAT-2, Korea Multi-Purpose Satellite-2) images at 1 to 4 m resolution were utilized to produce digital elevation and building surface models as well as land cover maps at very high(5m) resolution. The Climate Analysis Seoul(CAS) was used to calculate the fractional coverage of land cover classes in built-up areas and thermal capacity of the buildings from their areal volumes. It then produced analyzed maps of local-scale temperature based on the old and new input data. For the verification of the accuracy improvement by the precise input data, the analyzed maps were compared to the surface temperature derived from the ASTER satellite image and to the ground observation at our detailed study region. After the enhancement, the ASTER temperature was highly correlated with the analyzed temperature at building (BS) areas (R=0.76) whereas there observed no correlation with the old input data. The difference of the air temperature deviation was reduced from 1.27 to 0.70K by the enhancement. The enhanced precision of the input data yielded reasonable and more accurate local-scale temperature analysis based on realistic surface models in built-up areas. The improved analysis tools can help urban planners evaluating their design scenarios to be prepared for the urban climate.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.295-305
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• 2024

This study aims to analyze the micro-sedimentary structures of the Hwangdo tidal flats using ultra-high resolution unmanned aerial vehicle (UAV) data. Tidal flats, located in the transitional area between land and sea, constantly change due to tidal activities and provide a unique environment important for understanding sedimentary processes and environmental conditions. Traditional field observation methods are limited in spatial and temporal coverage, and existing satellite imagery does not provide sufficient resolution to study micro-sedimentary structures. To overcome these limitations, high-resolution images of the Hwangdo tidal flats in Chungcheongnam-do were acquired using UAVs. This area has experienced significant changes in its sedimentary environment due to coastal development projects such as sea wall construction. From May 17 to 18, 2022, sediment samples were collected from 91 points during field surveys and 25 in-situ points were intensively analyzed. UAV data with a spatial resolution of approximately 0.9 mm allowed identifying and extracting parameters related to micro-sedimentary structures. For mud cracks, the length of the major axis of the polygons was extracted, and the wavelength and ripple symmetry index were extracted for ripple marks. The results of the study showed that in areas with mud content above 80%, mud cracks formed at an average major axis length of 37.3 cm. In regions with sand content above 60%, ripples with an average wavelength of 8 cm and a ripple symmetry index of 2.0 were formed. This study demonstrated that micro-sedimentary structures of tidal flats can be effectively analyzed using ultra-high resolution UAV data without field surveys. This highlights the potential of UAV technology as an important tool in environmental monitoring and coastal management and shows its usefulness in the study of sedimentary structures. In addition, the results of this study are expected to serve as baseline data for more accurate sedimentary facies classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.537-549
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• 2023

The distribution characteristics of tidal flat sediment components are used as an essential data for coastal environment analysis and environmental impact assessment. Therefore, a reliable classification map of surface sedimentary facies is essential. This study evaluated the applicability of regression kriging to generate a classification map of the sedimentary facies of tidal flats. For this aim, various factors such as the number of field survey data and remote sensing-based auxiliary data, the effect of regression models on regression kriging, and the comparison with other prediction methods (univariate kriging and regression analysis) on surface sedimentary facies classification were investigated. To evaluate the applicability of regression kriging, a case study using unmanned aerial vehicle (UAV) data was conducted on the Hwang-do tidal flat located at Anmyeon-do, Taean-gun, Korea. As a result of the case study, it was most important to secure an appropriate amount of field survey data and to use topographic elevation and channel density as auxiliary data to produce a reliable tidal flat surface sediment facies classification map. In addition, regression kriging, which can consider detailed characteristics of the sediment distributions using ultra-high resolution UAV data, had the best prediction performance compared to other prediction methods. It is expected that this result can be used as a guideline to produce the tidal flat surface sedimentary facies classification map.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.