• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.231-240
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• 2023

In this research, our goal was to explore the potential use of cement mortar augmented with liquid red mud. To facilitate this, we neutralized liquid red mud(LR) - exhibiting a pH of 10-12 - using sulfuric acid to yield sulfuric acid neutralized red mud(SR). We then evaluated the flow, setting time, and compressive strength of the cement mortar combined with liquid red mud, while also performing a thorough examination of its chemical properties through X-ray diffraction(XRD) and scanning electron microscopy(SEM). The flow tests indicated a decrease in flow values for both MS-LR and MS-SR in comparison to the Plain. Analogously, the setting time for MS-LR and MS-SR was found to be abbreviated when juxtaposed with the Plain. With regards to compressive strength, MS-LR demonstrated a surge in strength at the 1-day mark, while MS-SR displayed a diminution at the 1-day and 3-day timepoints compared to the Plain. XRD analysis illustrated that after 28 days, the XRD patterns of Plain and MS-SR bore significant resemblance, though a new peak was detected in MS-LR. SEM imagery highlighted that the microstructures of Plain and MS-SR were alike, but MS-LR manifested a distinct microstructure, characterized by a finely fibrous formation. Based on these observations, we infer that the replacement of cement mortar with liquid red mud neutralized with sulfuric acid contributes to a noticeable enhancement in strength, thereby verifying its suitability for this application.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Korean Journal of Culture and Social Issue
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• v.20 no.2
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• pp.89-105
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• 2014

This study investigated the effects of a brief Loving-Kindness and Compassion Meditation(20 minutes) on compassionate love and altruism of undergraduate students. Among the 187 students who were completed the KIIP-SC, 35 of high scored students in Interpersonal Problem Scale were randomly assigned to one of two different groups: Loving-Kindness and Compassion Meditation group(LCM, n=19) and Imagery Trainning group(IT, n=16). During the 20 minutes of practice, LCM group was asked to practice loving-kindness and compassion meditation and IT group was asked to investigate personal characteristics of the imagined people who were same as LCM. Compassionate love, four immeasurebles, two kind of altruistic behaviors, and mood states were measured pre- and post-treatment. LCM group showed significantly higher incremental scores for compassionate love, donation for strange person, and helping behaviors for friends, for close person, and for stranger than IT group. Negative and positive mood states were improved for each of the groups, but there was no significant differences between groups. The results suggested that the comparatively short practice of loving-kindness and compassion meditation could increase altruistic attitudes and intentions toward other persons. And, these effects could not be attributed to mood effects. Limitations of this study and the directions of further research were discussed.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.135-142
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• 2023

A various technology of remote sensing and image analysis are applied to study landscape changes and their influencing factors in stream corridors. We developed a method to detect landscape changes over time by calculating the optical index using multispectral images taken from satellites at various time points, calculating the threshold to delineate the boundaries of water bodies, and creating binarized maps into land and water areas. This method was applied to the upstream reach of the weirs in the Geumgang River to track changes in the sandbar formed by the opening of the weir gate. First, we collected multispectral images with a resolution of 10 m × 10 m taken from the Sentinel-2 satellite at various times before and after the opening of the dam in the Geumgang River. The normalized difference water index (NDWI) was calculated using the green light and near-infrared bands from the collected images. The Otsu's threshold of NDWI calculated to delineate the boundary of the water body ranged from -0.0573 to 0.1367. The boundary of the water area determined by remote sensing matched the boundary in the actual image. A map binarized into water and land areas was created using NDWI and the Otsu's threshold. According to these results of the developed method, it was estimated that a total of 379.7 ha of new sandbar was formed by opening the three weir floodgates from 2017 to 2021 in the longitudinal range from Baekje Weir to Daecheong Dam on the Geumgang River. The landscape detection method developed in this study is evaluated as a useful method that can obtain objective results with few resources over a wide spatial and temporal range.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.875-883
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• 2023

In this letter, we used PlanetScope imagery to conduct experiments on the color characteristics for oil type classification of marine oil spills through Red-Green-Blue (RGB) histogram analysis. The histograms of marine oil spills can be divided into three categories (dark black tones, light silver tones, and light rainbow tones) according to the distribution of pixel values in each band. Thick oil layers with dark black tones can be classified as heavy oil, while thin oil layers with light silver and rainbow tones can be classified as light oil. As more images are analyzed in the future, these oil spill detection and classification methods will become more generalized and reliable.

• 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.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.