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  • April 30, 2018

    0 12 6

    Development of Image-map Generation and Visualization System Based on UAV for Real-time Disaster Monitoring

    Jangwoo Cheon*, Kyoungah Choi* and Impyeong Lee*†

    Korean Journal of Remote Sensing 2018; 34(2): 407-418

    https://doi.org/10.7780/kjrs.2018.34.2.2.8

    Abstract
    The frequency and risk of disasters are increasing due to environmental and social factors. In order to respond effectively to disasters that occur unexpectedly, it is very important to quickly obtain up-to-date information about target area. It is possible to intuitively judge the situation about the area through the image-map generated at high speed, so that it can cope with disaster quickly and effectively. In this study, we propose an image-map generation and visualization system from UAV images for realtime disaster monitoring. The proposed system consists of aerial segment and ground segment. In the aerial segment, the UAV system acquires the sensory data from digital camera and GPS/IMU sensor. Communication module transmits it to the ground server in real time. In the ground segment, the transmitted sensor data are processed to generate image-maps and the image-maps are visualized on the geo-portal. We conducted experiment to check the accuracy of the image-map using the system. Check points were obtained through ground survey in the data acquisition area. When calculating the difference between adjacent image maps, the relative accuracy was 1.58 m. We confirmed the absolute accuracy of the image map for the position measured from the individual image map. It is confirmed that the map is matched to the existing map with an absolute accuracy of 0.75 m. We confirmed the processing time of each step until the visualization of the image-map. When the image-map was generated with GSD 10 cm, it took 1.67 seconds to visualize. It is expected that the proposed system can be applied to real - time monitoring for disaster response.
  • December 31, 2022

    0 54 6

    Development of Marine Debris Monitoring Methods Using Satellite and Drone Images Heung-Min Kim 1)·Suho Bak 2)·Jeong-ik Han3)·Geon Hui Ye4

    김흥민 1)·박수호 2)·한정익3)·예건희4)·장선웅 5)†

    Korean Journal of Remote Sensing 2022; 38(6): 1109-1124

    https://doi.org/10.7780/kjrs.2022.38.6.1.12

    Abstract
    본 연구에서는 단시간 내 광범위한 지역에 대한 해양쓰레기 발생 실태 파악이 가능하도록 위성 및 드론 다중분광 영상을 이용한 해안쓰레기 모니터링 기법을 제안한다. Sentinel-2 위성 영상을 이용한 해안쓰레기 탐 지를 위해 multi-layer perceptron (MLP) 모델을 적용하였고, 드론 다중분광 영상을 이용한 해안쓰레기 탐지를 위해 딥러닝 모델 중 U-Net, DeepLabv3+ (ResNet50), DeepLabv3+ (Inceptionv3)의 탐지 성능평가 및 비교를 수행 하였다. 위성 영상을 이용한 해안쓰레기 탐지 결과 F1-Score 0.97을 보였다. 드론 다중분광 영상을 이용한 해안 쓰레기 탐지는 초목류와 플라스틱류에 대한 탐지를 수행하였고, 탐지 결과 DeepLabv3+ (Inceptionv3) 모델이 mean Intersection over Union (mIoU) 0.68로 가장 우수한 성능을 보였다. 초목류는 F1-Score 0.93, IoU는 0.86을 보인 반면에 플라스틱류의 F1-Score 0.5, IoU는 0.33으로 낮은 성능을 보였다. 그러나 플라스틱류 마스크 영상 생성을 위해 적용된 분광 지수식의 F1-Score는 0.81로 DeepLabv3+ (Inceptionv3)의 플라스틱류 탐지 성능보다 높은 성능을 보이며, 분광 지수식을 이용한 플라스틱류 모니터링이 가능할 것으로 판단된다. 본 연구에서 제안 된 해안쓰레기 모니터링 기법을 통해 해안쓰레기 발생에 대한 정량적 자료 제공과 더불어 해안쓰레기 수거·처 리 계획 수립에 활용할 수 있다.
  • ReviewOctober 31, 2024

    0 1162 63

    National Disaster Management and Monitoring Using Satellite Remote Sensing and Geo-Information

    Jongsoo Park , Hagyu Jeong , Junwoo Lee

    Korean Journal of Remote Sensing 2024; 40(5): 813-832

    https://doi.org/10.7780/kjrs.2024.40.5.2.9

    Abstract
    As disasters become more diverse and widespread, disaster management at the national level is increasingly important. Since satellite remote sensing technology is capable of observing a wide range of areas on a regular basis, it can be effectively utilized to monitor a massive scale of disaster conditions and preemptively respond to urgent disasters. Disasters that can be managed through satellite remote sensing technology include forest fires, drought, floods, landslides, and earthquakes. This study introduces a variety of studies using satellite remote sensing and Geographic Information System (GIS) as well as a number of actual cases utilizing the above. However, with the satellites currently in operation, there are difficulties in detecting and analyzing the disasters above due to the limitations on spatial and temporal resolutions. Recently, new measures have been developed to overcome such limitations through the development and constellation operation of microsatellites. In addition, new technologies are under development where a massive quantity of satellite images is analyzed by Artificial Intelligence (AI) technology. It is expected that temporal and spatial limitations can be addressed through satellite-developed and constellation systems in the future, which would lead to scientific disaster management through grafting with AI technology.
  • Research ArticleDecember 31, 2024

    0 158 22

    SAMBA: Synthetic Data-Augmented Mamba-Based Change Detection Algorithm Using KOMPSAT-3A Imagery

    Rogelio Ruzcko Tobias , Sejeong Bae , Hwanhee Cho , Jungho Im

    Korean Journal of Remote Sensing 2024; 40(6): 1505-1521

    https://doi.org/10.7780/kjrs.2024.40.6.3.11

    Abstract
    Change detection is essential for applications such as urban planning, environmental monitoring, and disaster response. Despite advancements in high-resolution satellite imagery, accurate change detection remains challenging due to increased landscape heterogeneity and variable atmospheric conditions. The Mamba model, an efficient state-space model-based architecture, has shown promise in capturing spatiotemporal relationships in high-resolution datasets, addressing the limitations of traditional methods that struggle with the diverse appearances of urban structures. This research investigates applying Mamba to multitemporal Korea Multi-Purpose Satellite (KOMPSAT) imagery, using both real and synthetic data from SyntheWorld, a dataset developed to simulate various change scenarios. This study introduces a synthetic data-augmented mamba-based change detection algorithm (SAMBA), designed to detect structural changes in urban environments using KOMPSAT-3A satellite imagery. The main objectives are to evaluate the Mamba binary change detection (MambaBCD) model’s ability to detect building changes in KOMPSAT-3A images and assess the impact of synthetic data augmentation on performance. Experimental results with MambaBCD-Small and MambaBCD-Tiny models indicate that synthetic data incorporation improves generalization in complex settings, achieving high performance across multiple data and model configurations. Notably, the MambaBCD-Tiny model, with or without synthetic augmentation, outperformed the larger-parameter MambaBCD-Small model, demonstrating enhanced sensitivity in detecting satellite image changes. Performance evaluation metrics yielded an overall accuracy of 99.73%, precision of 98.34%, recall of 96.54%, F1-score of 97.43%, intersection over union of 95.00%, and Kappa coefficient of 97.29%. These metrics were similarly used to test the SAMBA algorithm’s generalization on benchmark change detection datasets, showcasing its potential as a robust tool for highresolution image change detection.
KSRS
December 2024 Vol. 40, No. 6, pp. 881-1521

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