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Deep learning based Land Cover Change Detection Using U-Net
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U-Net을 이용한 딥러닝 기반의 토지피복 변화탐지

논문 기본 정보

Type
Academic journal
Author
Wonho Jo (서울대학교) Key-Ho Park (서울대학교)
Journal
The Korean Geographlcal Society Journal of the Korean Geographical Society Vol.57 No.3(Wn.210) KCI Excellent Accredited Journal
Published
2022.6
Pages
297 - 306 (10page)

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Method
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Deep learning based Land Cover Change Detection Using U-Net
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Abstract· Keywords

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Remote sensing enables the iterative collection of data, which enables spatio-temporal analysis using satellite images. Detecting land cover changes using multi-time satellite imagery plays an important role in efficient land monitoring and planning. In the existing land cover change detection method, effective change detection was difficult on account of using a parameter-based model. This study proposes a new method of land cover change detection by presenting a model based on U-Net deep learning architecture. To verify the effectiveness of model presented in this study, change detection was tested in two cases according to the type of change in land cover. The model improved the efficiency by synchronizing the detection and segmentation operations, and showed effective detection results. The approach of this study can be a useful model for monitoring and managing the land.
#딥러닝 #완전합성곱 신경망 #원격탐사 #토지피복 #변화탐지 #deep learning #fully Convolutional network #remote sensing #land cover #change detection

Contents

요약
Abstract
1. 서론
2. 연구대상지 및 데이터
3. U-Net 기반의 토지피복 변화탐지
4. 결론
참고문헌

References (22)

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산림청, 2020, 2019년도 산불통계 연보. google schola 조명래･송두범･강현수, 2010, 세종시와 충남의 상생발전 모색을 위한 심포지엄. google schola 조원호･임용호･박기호, 2019, “합성곱 신경망을 이용한 딥러닝 기반의 토지피복 분류: 한국 토지피복을 대상으로,” 대한지리학회지 54, 1-16. dbpia Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., and Gibbs, H. K., 2005, Global consequences of land use, Science, 309, 570-574. google schola Gärtner, P., Förster, M., Kurban, A., and Kleinschmit, B. 2014, Object based change detection of Central Asian Tugai vegetation with very high spatial resolution satellite imagery, International Journal of Applied Earth Observation and Geoinformation, 31, 110-121. google schola

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