Classification of Natural and Artificial Forests from KOMPSAT-3/3A/5 Images Using Artificial Neural Network |
Lee, Yong-Suk
(Department of Geoinformatics, University of Seoul)
Park, Sung-Hwan (Department of Geoinformatics, University of Seoul) Jung, Hyung-Sup (Department of Geoinformatics, University of Seoul) Baek, Won-Kyung (Department of Geoinformatics, University of Seoul) |
1 | Hay, G. J., K. O. Niemann, and G.F. McLean, 1996. An object-specific image-texture analysis of Hresolution forest imagery, Remote Sensing of Environment, 55(2): 108-122. DOI |
2 | Herrmann, I., A. Pimstein, A. Karnieli, Y. Cohen, V. Alchanatis, and D. J. Bonfil, 2010. LAI assessment of wheat and potato crops by and Sentinel-2 bands, Remote Sensing of Environment, 115: 2141-2151. |
3 | Huang, C., K. Song, S. Kim, J. R. G. Townshend, R. Davis, J. G. Masek, and S. N. Goward, 2008. Use of a dark object concept and support vector machines to automate forest cover change analysis, Remote Sensing of Environment, 112(3): 970-985. DOI |
4 | Hwang, J. I., 2018. Ship detection from single and dual polarized X-band SAR images using machine learning techniques, Seoul National University, Seoul, Korea (in Korean with English abstract). |
5 | KIM, D. S., 2017. Oil spill detection from dual-polarized SAR images using artificial neural network, Seoul National University, Seoul, Korea (in Korean with English abstract). |
6 | Kim, D. S., H. S. Jung, and W. K. Baek, 2016. Comparative Analysis among Radar Image Filters for Flood Mapping, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, 34(1): 43-52 (in Korean with English abstract). DOI |
7 | Kim, E. S., C. M. Kim, K. M. Kim, J. H. Ryu, J. S. Lim, and J. C. Kim, 2015. The Change of Korean National Forest Inventory System (1971-2010), Korea Forest Institute, Seoul, Korea (in Korean). |
8 | Kim, S. H., J. C. Kim, J. H. Ryu, J. S. Kim, S. A. Seo, H. K. Cho, B. O. Yoo, W. B. Sim, J. H. Seong, B. B. Park, J. S. Lim, I. B. Jeong, and J. W. Shin, 2011. Guide Book for the Sixth Korean National Forest Inventory and Fieldwork for Forest Health and Vitality, Korea Forest Institute, Seoul, Korea (in Korean). |
9 | Kwon, S. K., 2018. Classification of Natural Forest/Artificial Forest from Sentinel-2 Images Using Artificial Neural Network, Seoul National University, Seoul, Korea (in Korean with English abstract). |
10 | Kwon, S. K., H. S. Jung, W. K. Baek, and D. S. Kim, 2017. Classification of Forest Vertical Structrue in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network, Applied Science, 7(10): 1046. DOI |
11 | Kang, H. Y., I. J. Kang, H. Choi, and S. C. Lee, 2000. The Estatimate of Image Classification Accuracy Using Artificial Neural Networks, Journal of The Korean Society Of Civil Engineers, 2000(4): 585-588. |
12 | Lee, S. G., H. J. Lee, C. W. Sung, C. H. Park, W. S. Cho, and Y. S. Kim, 2010. A Topographical Classifier Development Support System Cooperating with Data Mining Tool WEKA from Airborne LiDAR Data, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, 28(1): 133-142 (in Korean with English abstract). |
13 | Manqi, L., J. H. Im, and C. Beier, 2013. Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest, GIScience & Remote Sensing, 50(4): 361-384. DOI |
14 | Markus, I., F. Vuolo, and C. Atzberger, 2016. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe, Remote Sensing, 8(3). |
15 | McClelland, J. L., D. E. Rumelhart, and G. E. Hinton, 1986. Parallel distributed processing: explorations in the microstructure of cognition. Volume 1. Foundations, United States. |
16 | McCulloch, W. S, and W. Pitts, 1943. A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5(4): 115-113. DOI |
17 | Solaris, 2017. History of Deep Learning - From Perceptron to GAN (Generative Adversarial Networks) - 1/2, http://solarisailab.com/archives/1206, Accessed on Dec. 4, 2018. |
18 | Murai, H. and Y. Iwasaki, 1975. Studies on function of water and soil conservation based on forest land part 1 influence of difference in forest condition upon water runoff infiltration and soil erosion, Bulletin for the Forestry and Forest Products Research Institute, 274: 23-84. |
19 | Park, S. G. and H. M. Kang, 2015. Characteristics of Vegetation Structure in Chamaecyparis Obtusa Stands1, Korean Journal of Environment and Ecology, 29(6): 907-916 (in Korean with English abstract). DOI |
20 | SlideServe, 2014. Comparison of Grade and Vegetation Index of Evergreen Forest in Gangwon Province Using Forest Geographic Information System and Landsat Satellite Image, https://www.slideserve.com/latham/landsat, Accessed on Dec. 14, 2018. |
21 | Teillet, P. M., B. Guindon, and D. G. Goodenough, 1982. On the Slope-Aspect Correction of Multispectral Scanner Data, Canadian Journal of Remote Sensing, 8(2): 84-106. DOI |
22 | Witten, I. h. and E. Frank, 2005. Data Mining: Practial Machine Learning Tools and Technique (2E), Elsevier, Atlanta, GA, USA. |