• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.395-408
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• 2015

High-resolution satellite image mosaics are becoming increasingly important in the field of remote sensing image analysis as an essential image processing to create a large image constructed from several smaller images. In this paper, we present an automatic seamline extraction technique and the procedure to generate a mosaic image by this technique. For more effective seamline extraction in the overlap region of adjacent images, an NDVI-based seamline extraction technique is developed, which takes advantage of the computational time and memory. The Normalized Difference Vegetation Index(NDVI) is an index of plant "greeness" or photosynthetic activity that is employed to extract the initial seamline. The NDVI can divide into manmade region and natural region. The cost image is obtained by the canny edge detector and the buffering technique is used to extract the ranging cost image. The seamline is extracted by applying the Dijkstra algorithm to a cost image generated through the labeling process of the extracted edge information. Histogram matching is also conducted to alleviate radiometric distortion between adjacent images acquired at different time. In the experimental results using the KOMPSAT-2/3 satellite imagery, it is confirmed that the proposed method greatly reduces the visual discontinuity caused by geometric difference of adjacent images and the computation time.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.609-625
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• 2022

Validation of satellite data products is critical for subsequent analysis that is based on the data. Particularly, performance of ocean color products in turbid and shallow near-land ocean areas has been questioned for long time for its difficulty that stems from the complex optical environment with varying distribution of water constituents. Furthermore, validation with ship-based or station-based measurements has also exhibited clear limitation in its spatial scale that is not compatible with that of satellite data. This study firstly performed validation of major GOCI-II products such as remote sensing reflectance, chlorophyll-a concentration, suspended particulate matter, and colored dissolved organic matter, using the in-situ measurements collected from ship-based field campaign. Secondly, this study also presents preliminary analysis on the use of drone images for product validation. Multispectral images were acquired from a MicaSense RedEdge camera onboard a UAV to compensate for the significant scale difference between the ship-based measurements and the satellite data. Variation of water radiance in terms of camera altitude was analyzed for future application of drone images for validation. Validation conducted with a limited number of samples showed that GOCI-II remote sensing reflectance at 555 nm is overestimated more than 30%, and chlorophyll-a and colored dissolved organic matter products exhibited little correlation with in-situ measurements. Suspended particulate matter showed moderate correlation with in-situ measurements (R²~0.6), with approximately 20% uncertainty.

• Journal of the Korean Society of Food Culture
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• v.3 no.1
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• pp.89-93
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• 1988

Because the production facilities of the Korean convenient food companies are placed in one space, the final products could be easily contaminated. It is necessary that the work space should be devided into contaminated zone, semisanitary zone and sanitary zone. The layout of the preparation facilities are reconsidered. Requirements for equipment and the facilities criteria be complemented with the air clean unit, and chilling refrigerator for rapid chilling of boiled rice and the cooked dishes for the assurance of the microbiological guality of foods. The equipment and the work space of the model companies which have the area of $99m^2,\;200m^2\;and\;300m^2$ are properly placed and designed in accordance with the regulations of the food sanitation and the architecture. (Packaging Meal Production Facilities).

• Spatial Information Research
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• v.3 no.2
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• pp.91-102
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• 1995

The purposes of this paper are to find ways to detect ecotone between two eco'||'&'||'not;systems, measure the width and size of ecotone around the Mt. Pukhansan National Park, and investigate environmental impacts, if any, on the forest ecosystem of the park by human activities. Normalized Difference Vegetation Index(NDVI) derived from TM data and the ana'||'&'||'not;lytical capabilities of GIS are used to investigate characteristics of the ecotone, or the impact zone, of the park. Major findings of the study can be summarized as follows: First, it was found that ecotone of the park could be identified from NDVI -distance curves deri"ed by a series of buffering op'||'&'||'not;erations. Second, NDVIs of all three years of the national park are about 14 percent higher than surrounding areas. Third, width of ecotone were found to be closely related to phenology, adjacent land use, environmental degradation, etc. Third, ecotone of the study area was nearly douvled during 1985-1993 period, which might be caused by heavy trampling of visitors. Thus it can be concluded that further studies are needed to find exact causes of the deterioration of plant communities of the ecotone of the park.

• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.22-30
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• 2011

In the construction operation, foundation work should be done in advance for the building structure to be installed. This foundation work include a number of activities such as excavation, ground water prevention, piling, wale installation, etc. Caution should be taken in the operation because the dynamics of earth movement can cause a significant failure in the temporary structure. The temporary structure, therefore, should be constantly monitored to understand its behavior. This paper introduces the USN-based monitoring system to automatically identify the behavior of the temporary structure in addition to visual inspection. The autonomous capability of the monitoring system can increase the safety in the construction operation by providing the detailed structural changes of temporary structures.

• Tunnel and Underground Space
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• v.29 no.2
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• pp.108-123
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• 2019

In recent years, as safety management in underground mines has become more important in the worldwide, mine safety management technologies combining information communication technology such as real-time worker position tracking, monitoring system and equipment remote control have been developed. Wireless communication system is mainly applied to these technologies for the flexibility of network configuration. There are some cases the monitoring system was installed in domestic underground mines, but, it is necessary to develop the technology more suitable for domestic mining standard. In this study, we developed the real-time environmental monitoring system using ZigBee technology and examined the result of application to domestic limestone mine. Furthermore, applicability of the developed environment monitoring system to $VentSim^{TM}$ LiveView was checked. This study is expected to contribute to the related studies like the optimization of the ventilation system in underground mines.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1273-1281
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• 2023

Absolute radiometric calibration is a crucial process in converting the electromagnetic signals obtained from satellite sensors into physical quantities. It is performed to enhance the accuracy of satellite data, facilitate comparison and integration with other satellite datasets, and address changes in sensor characteristics over time or due to environmental conditions. In this study, field campaigns were conducted to perform vicarious calibration for the multispectral channels of the CAS500-1. Two valid field observations were obtained under clear-sky conditions, and the top-of-atmosphere (TOA) radiance was simulated using the MODerate resolution atmospheric TRANsmission 6 (MODTRAN 6) radiative transfer model. While a linear relationship was observed between the simulated TOA radiance of tarps and CAS500-1 digital numbers(DN), challenges such as a wide field of view and saturation in CAS500-1 imagery suggest the need for future refinement of the calibration coefficients. Nevertheless, this study represents the first attempt at absolute radiometric calibration for CAS500-1. Despite the challenges, it provides valuable insights for future research aiming to determine reliable coefficients for enhanced accuracy in CAS500-1's absolute radiometric calibration.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.