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NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.38 no.5 pt.1, 2022년, pp.545 - 557
이슬기 (강원대학교 스마트지역혁신학과) , 송종성 (강원대학교 과학교육학부) , 이창욱 (강원대학교 과학교육학부) , 고보균 (강원대학교 과학교육학부)
This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images....
Choi, J.W., Y.G. Byun, Y.I. Kim, and K.Y. Yu, 2006. Support vector machine classification of hyperspectral image using spectral similarity kernel, Journal of Korean Society for Geospatial Information Science, 14(4): 71-77 (in Korean with English abstract).
Hsu, C.-W., C.-C. Chang, and C.-J. Lin, 2003. A practical guide to support vector classification, National Taiwan University, Taipei, Taiwan.
In, C.G., 2013. A Study on the Systematic Management Measures of Natural Resources in the Demilitarizes Zone - Based on the Demilitarizes Zone in Gyeonggi-Do, Dankook University, Yongin, Korea (in Korean with English abstract).
Jo, M.H., S.J. Kim, D.Y. Kim, and K.S. Choi, 2012. Comparative analysis of classification accuracy for calculating cropland areas by using satellite images, Journal of the Korean Society of Agricultural Engineers, 54(2): 47-53 (in Korean with English abstract). https://doi.org/10.5389/ksae.2012.54.2.047
Jang, S.S., 2018. Classification of Tree Species Using Sentinel-2 Satellite Image, The University of Seoul, Seoul, Korea (in Korean with English abstract).
Kang, N.Y., S.Y. Go, and G.S. Cho, 2013. A comparative study on suitable SVM kernel function of land cover classification using KOMPSAT-2 imagery, Journal of Korean Society for Geospatial Information Science, 21(2): 19-25 (in Korean with English abstract). https://doi.org/10.7319/kogsis.2013.21.2.019
Kim, Y., G. Kwak, K.D. Lee, S.I. Na, C.W. Park, and N.W. PARK, 2018. Performance evaluation of machine learning and deep learning algorithms in crop classification: impact of hyper-parameters and training sample size, Korean Journal of Remote Sensing, 34(5): 811-827 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2018.34.5.9
Korea Forest Service, 2020. 2020 Annual Report on Wildfire Statistics, https://www.forest.go.kr/kfsweb/cop/bbs/selectBoardArticle.do?bbsIdBBSMSTR_1008&mnNKFS_06_09_05&nttId3156244, Accessed on Oct. 14, 2022.
Korea Forest Service, 2021. 2021 Annual Report on Wildfire Statistics, https://www.forest.go.kr/kfsweb/cop/bbs/selectBoardArticle.do?bbsIdBBSMSTR_1008&mnNKFS_06_09_05&nttId3169550, Accessed on Oct. 14, 2022.
Kwak, J.H., 2015. Comparative analysis of Difference of Normalized Difference Vegetation Index between Landsat 7 ETM+ and Landsat 8 OLI Sensors, Kumoh National Institute of Technology, Gumi, Korea (in Korean with English abstract).
Kwon, S.K., E.H. Kim, J.B. Lim, and A.R. Yang, 2021. The analysis of changes in forest status and deforestation of North Korea's DMZ using rapideye satellite imagery and google earth, Journal of the Korean Association of Geographic Information Studies, 24(4): 113-126 (in Korean with English abstract). https://doi.org/10.11108/kagis.2021.24.4.113
Lee, Y.K., 2021. Optimization of Machine Learning Algorithms for Land use and Land cover Classification using Remotely-sensed Data, Kangwon National University, Chuncheon, Korea.
Melgani, F. and L. Bruzzone, 2004. Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1778-1790. https://doi.org/10.1109/tgrs.2004.831865
Maulik, U. and D. Chakraborty, 2017. Remote sensing image classification: a survey of support-vectormachine-based advanced techniques, IEEE Geoscience and Remote Sensing Magazine, 5(1): 33-52. https://doi.org/10.1109/mgrs.2016.2641240
Park, S.W., S.J. Lee, C.Y. Chung, S.R. Chung, I.C. Shin, W.C. Jung, H.S. Mo, S.I. Kim, and Y.W. Lee, 2019. Satellite-based forest withering index for detection of fire burn area: its development and application to 2019 Kangwon wildfires, Korean Journal of Remote Sensing, 35(2): 343-346 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.2.13
Roteta, E. and P. Oliva, 2020. Optimization of a random forest classifier for burned area detection in chile using Sentinel-2 data, Proc. of 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile, Mar. 22-26, pp. 568-573. https://doi.org/10.1109/lagirs48042.2020.9165585
Song, J.Y., J.C. Jeong, and S.H. Lee, 2018. Development of a classification method for forest vegetation on the stand level, using KOMPSAT-3A imagery and land coverage map, Korean Journal of Environment and Ecology, 32(6): 686-697 (in Korean with English abstract). https://doi.org/10.13047/KJEE.2018.32.6.686
Schmidhuber, J., 2015. Deep learning in neural networks: an overview, Neural Networks, 61: 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
Youn, H.J., 2020. Building Sentinel-2 Forest Fire Grade Classification Model using SVM Machine Learning Algorithm, Namseoul University, Cheonan, Korea.
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