참고문헌
- Hong, S.E., Yi, D.H., and Park, S.H. (2004), Non-coincidence Measurements Using High Resolution Satellite Images and Digital Topographic Maps, The Journal of GIS Association of Korea, Vol. 12, No. 1, pp. 43-56. (in Korean with English abstract).
- Lee, G.S., Kim, Y.R., Sim, J.M., and Min, K.S. (2009), The Analysis of Land Category Information for Flood Inundation Area Based on GIS, Journal of The Korean Cadastre Information Association, vol.11, no.2, pp. 45-55. (in Korean with English abstract).
- Lee, H.J., Lu, J.H., and Kim, S.Y. (2011.3), Land Cover Objectoriented Base Classification Using Digital Aerial Photo Image, The Korean Society for Geospatial Information Science, vol.19, no.1, pp. 105-113. (in Korean with English abstract).
- Lee, I.S. and Hyun, C.U. (2014), Applicability of Hyperspectral Imaging Technology for the Check of Cadastre's Land Category, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 4-2, pp. 421-430. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2014.32.4-2.421
- Jensen, J.R. (2004), Introductory Digital Image Processing - a Remote Sensing Perspective, 3rd edition, Prentice Hall Inc.
- Rhee, S.Y., Jeon, W.S., and Choi, H. (2018), Analysis on the applicability of deep learning for Kompsat-3A satellite image classification, Journal of the Korean Society for Geospatial Information Science, Vol. 26 No. 4, pp. 69-76. (in Korean with English abstract) https://doi.org/10.7319/kogsis.2018.26.4.069
- Lee, S.H. and Kim, J.S. (2019), Land Cover Classification Using Semantic Image Segmentation with Deep Learning, Korean Journal of Remote Sensing, Vol. 35, No. 2, pp. 279-288. (in Korean with English abstract) https://doi.org/10.7780/KJRS.2019.35.2.7
- Park, J.M., Sim, W.D., and Lee, J.S. (2019), Automatic Classification by Land Use Category of National Level LULUCF Sector using Deep Learning Model, Korean Journal of Remote Sensing, Vol. 35, No. 6-2, pp. 1053-1065. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2019.35.6.2.3
- LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998), Gradient-based learning applied to document recognition. Proceedings of the IEEE, Vol. 86, No.11, pp. 2278-2324. https://doi.org/10.1109/5.726791
- Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv, https://arxiv.org/abs/1409.1556
- LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, and W., Jackel, L.D., (1989), Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, Volume 1, Issue 4, Dec., pp. 541 - 551. https://doi.org/10.1162/neco.1989.1.4.541
- Behnke, S. (2003), Hierarchical Neural Networks for Image Interpretation, Lecture Notes in Computer Science, Draft submitted to Springer-Verlag. Vol. 2766
- Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., and Fei, L.F. (2014), ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, Vol. 115, Issue. 3, pp. 211-252. https://doi.org/10.1007/s11263-015-0816-y
- Song, A.R., and Kim, Y.I. (2017), Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems, Korean Journal of Remote Sensing, v. 33 no. 6 pt. 2, pp. 1061-1073. (in Korean with English abstract)