1 |
Li, X. and G. Shao, 2013. Object-based urban vegetation mapping with high-resolution aerial photography as a single data source, International Journal of Remote Sensing, 34(3): 771-789.
DOI
|
2 |
Ministry of Land, Infrastructure and Transport, 2019 Cadastral Statistics yearbook, SeJong, Korea.
|
3 |
Murray, N.J., D.A. Keith, D. Simpson, J.H. Wilshire, and R.M. Lucas, 2018. An online remote sensing application for land cover classification and monitoring, Methods in Ecology and Evolution, 9(9): 2019-2027.
DOI
|
4 |
Rwanga, S. and J.M. Ndambuki, 2017. Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS, International Journal of Geosciences, 8(4): 611-622.
DOI
|
5 |
Scott, G.J., M.R. England, W.A. Starms, R.A. Marcum, and C.H. Davis, 2017. Training Deep Convolutional Neural Networks for Land-Cover Classification of High-Resolution Imagery, Geoscience and Remote Sensing Letters, 14(4): 549-553.
DOI
|
6 |
Statistics Finland, 2019. Greenhouse Gas Emissions in Finland 1990 to 2017, Statistics Finland, Helsinki, Finland.
|
7 |
Song, A.R. and Y.I. Kim, 2017. Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems, Korean Journal of Remote Sensing, 33(6): 1061-1073 (in Korean with English abstract).
DOI
|
8 |
Spanhol, F.A., L.S. Oliveira, C. Petitjean, and L. Heutte, 2016. Breast cancer histopathological image classification using Convolutional Neural Networks, Proc. of 2016 International Joint Conference on Neural Networks, Vancouver, Canada, Jul. 24-29, pp. 2560-2567.
|
9 |
Statistics Korea, 2019. Agricultural Area Survey in 2018, Statistics Korea, Daejeon, Korea.
|
10 |
Szegedy, C., S. Loffe, V. Vanhoucke, and A.A. Alemi, 2017. Inception-v4, inception-resnet and the impact of residual connections on learning, Proc. of Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, USA, Feb. 4-9.
|
11 |
Wang, S., W. Liu, J. Wu, L. Cao, Q. Meng, and P.J. Kennedy, 2016. Training deep neural networks on imbalanced data sets, Proc. of 2016 International Joint Conference on Neural Networks, Vancouver, Canada, Jul. 24-29, pp. 4368-4374.
|
12 |
Welh, R.C. and N.D. Riggan, 2010. Object-based classification vs. pixel-based classification: comparative importance of multi-resolution imagery, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4): C7.
|
13 |
Xia, X., Y. Wu, Q. Lu, and C. Fan, 2019. Experimental study on crop disease detection based on deep learning, IOP Conference Series: Materials Science and Engineering, 569(5): 052034.
DOI
|
14 |
Myint, S.W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sensing of Environment, 115(5): 1145-1161.
DOI
|
15 |
Oh, C.Y., S.Y. Park, H.S. Kim, and C.U. Choi, 2010. Comparison of Landcover Map Accuracy Using High Resolution Satellite Imagery, The Korean Association of Geographic Information Studies, 13(1): 89-100 (in Korean with English abstract).
|
16 |
Park, J.W., H.S. Na, and J.S. Yim, 2017. Comparison of Land-use Change Assessment Methods for Greenhouse Gas Inventory in Land Sector, Journal of Climate Change Research, 8(4): 329-337 (in Korean with English abstract).
DOI
|
17 |
Park, S.J., C.H. Lee, M.S. Kim, S.G. Yun, Y.H. Kim, and B.G. Ko, 2016. Calculation of GHGs Emission from LULUCF-Cropland Sector in South Korea, Korean Society of Soil Science and Fertilizer, 49(6): 826-831 (in Korean with English abstract).
DOI
|
18 |
Korea Forest Service, 2017. 7th National Forest Resource Survey and Forest Health and Vitality Survey Guideline, Korea Forest Service, Daejeon, Korea.
|
19 |
Perez, L. and J. Wang, 2017. The Effectiveness of Data Augmentation in Image Classification using Deep Learning, arXiv preprint arXiv:1712.04621.
|
20 |
Rouhi, R., M. Jafari, S. Kasaei, and P. Keshavarzian, 2015. Benign and malignant breast tumors classification based on region growing and CNN segmentation, Expert Systems with Applications, 42(3): 990-1002.
DOI
|
21 |
Korea Forest Service, 2019. 4th Forest Aerial Photography, http://www.forest.go.kr/newkfsweb/html/HtmlPage.do?pg=/fgis/UI_KFS_5003_010300.html&mn=KFS_02_04_03_04_08&orgId=fgis/, Accessed on Mar. 5, 2019.
|
22 |
Chitroub, S., 2010. Classifier combination and score level fusion: concepts and practical aspects, International Journal of Image and Data Fusion, 1(2): 113-135.
DOI
|
23 |
Chung, Y. J., S.M. Ahn, J.H. Yang, and J.J. Lee, 2017. Comparison of Deep Learning Frameworks: About Theano, Tensorflow, and Cognitive Toolkit, Journal of Intelligence and Information Systems, 23(2): 1-17 (in Korean with English abstract).
DOI
|
24 |
Cai, G., H. Ren, L. Yang, N. Zhang, M. Du, and C. Wu, 2019. Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme, Sensors, 19(14): 3120.
DOI
|
25 |
Garciaoliva, F. and O.R. Masera, 2004. Assessment and measurement issues related to soil carbon sequestration in land-use, land-use change, and forestry (LULUCF) projects under the Kyoto Protocol, Climatic Change, 65(3): 347-364.
DOI
|
26 |
Hu. J., L. Shen, and G. Sun, 2018. Squeeze-and-Excitation Networks, Proc. of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, Jun. 18-22, pp. 7132-7141.
|
27 |
Bergado, J.R., C. Persello, J. Ray, and C. Gevaert, 2016. A deep learning approach to the classification of sub-decimetre resolution aerial images, Proc. of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, Jul. 10-15, pp. 1516-1519.
|
28 |
Huang, M.H. and R.T. Rust, 2018. Artificial Intelligence in Service, Journal of Service Research, 21(2): 155-172.
DOI
|
29 |
IPCC, 2009. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, vol. 4, IPCC, Geneva, Switzerland.
|
30 |
Achard, F., G. Grassi, M. Herold, M. Teobaldelli, and D. Mollicone, 2008. Use of satellite remote sensing in LULUCF sector, Proc. of the IPCC Expert Meeting, Jena, Germany, May 13-15, vol. 33, pp. 1-25
|
31 |
IPCC, 2019. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, vol. 4, IPCC, Geneva, Switzerland.
|
32 |
Jo, W.H., W.H. Lim, and K.H. Park, 2019. Deep learning based Land Cover Classification Using Convolutional Neural Network: a case study of Korea, The Korean Geographical Society, 54(1): 1-16 (in Korean with English abstract).
|
33 |
Johnsson, K., 1994. Segment-Based Land-Use Classification from SPOT Satellite Data, Photogrammetric Engineering and Remote Sensing, 60(1): 47-54.
|
34 |
Ke, Y., L.J. Quackenbush, and J. Im, 2010. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification, Remote Sensing of Environment, 114(6): 1141-1154.
DOI
|
35 |
Kim, C. M., 2016. Land use classification and land use change analysis using satellite images in Lombok Island, Indonesia, Forest Science and Technology, 12(4): 183-191.
DOI
|
36 |
Lee, H.J., J.H. Ru, and Y.G. Yu, 2010. Extracting High Quality Thematic Information by Using High-Resolution Satellite Imagery, Journal of the Korean Society for Geospatial Information Science, 18(1): 73-81 (in Korean with English abstract).
|
37 |
Kim, Y. H., 2019. Age Estimation Method based on Comparative Convolutional Neural Network using Inception Module, Master's thesis, Kyungpook National University, Daegu, Korea (in Korean with English abstract).
|
38 |
Yeom., J.H., J.H. Lee, D.J. Kim, and Y.I. Kim, 2011. Hierarchical Land Cover Classification using IKONOS and AIRSAR Images, Korean Journal of Remote Sensing, 27(4): 435-444 (in Korean with English abstract).
DOI
|
39 |
Yim., J.S., R.H. Kim, S.J. Lee, and Y.M. Son, 2015. Land-use Change Assessment by Permanent Sample Plots in National Forest Inventory, Journal of Climate Change Research, 6(1): 33-40 (in Korean with English abstract).
DOI
|
40 |
Yu, S.C., W. Ahn, and J.A. Ok, 2015. A Study on Construction Plan of the Statistics for National Green House Gas Inventories (LULUCF Sector), Journal of Korean Society for Geospatial Information Science, 23(3): 67-77 (in Korean with English abstract).
DOI
|
41 |
Lee, J.Y. and T.A. Warner, 2006. Segment based image classification, International Journal of Remote Sensing, 27(16): 3403-3412.
DOI
|
42 |
Lee, S.H. and J.S. Kim, 2019. Land Cover Classification Using Sematic Image Segmentation with Deep Learning, Korean Journal of Remote Sensing, 35(2): 279-288 (in Korean with English abstract).
DOI
|
43 |
Li, W., H. Fu, L. Yu, and A. Cracknell, 2017. Deep learning based oil palm tree detection and counting for high resolution remote sensing image, Remote Sensing, 9(1): 22
DOI
|