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2021 Vol.8, Issue 4 Preview Page

Original Article

31 December 2021. pp. 253-265
Chen, Z. and Ho, P.H. 2019. Global-connected network with generalized ReLU activation. Pattern Recognition 96, 106961. doi:10.1016/j.patcog.2019.07.006. 10.1016/j.patcog.2019.07.006
Jeong, C.H. and Park, J.H. 2021. Analysis of Growth Characteristics Using Plant Height and NDVI of Four Waxy Corn Varieties Based on UAV Imagery. Korean Journal of Remote Sensing 37(4): 733-745. doi:10.7780/ KJRS.2021.37.4.5
Keras. The Python Deep Learning API. Accessed 12 August 2021.
Kim, Y.S., Park, N.W., Hong, S.Y., Lee, K.D. and Yoo, H.Y. 2014. Early production of large-area crop classification map using time-series vegetation index an past crop cultivation patterns-A case study in Iowa State, USA. Korean Journal of Remote Sensing 30(4): 493-503. (in Korean) doi:10.7780/kjrs.2014.30.4.7 10.7780/kjrs.2014.30.4.7
Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters 14(5): 778-782. doi:10.1109/ LGRS.2017.2681128 10.1109/LGRS.2017.2681128
Lee, D.H., Shin, H.S. and Park, J.H. 2020. Developing a p-NDVI Map for Highland Kimchi Cabbage Using Spectral Information from UAVs and a Field Spectral Radiometer. Agronomy 10(11): 1798. doi:10.3390/agronomy 10111798 10.3390/agronomy
Lee, D.H, Kim H.J. and Park J.H. 2021. UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area. Agronomy. 2021; 11(8): 1554. doi:10.3390/agronomy 11081554 10.3390/agronomy
Lee, S.H. and Lee, M.J. 2020. A Study on Deep Learning Optimization by Land Cover Classification Item Using Satellite Imagery. Korean Journal of Remote Sensing 36(6-2): 1591-1604. (in Korean) doi:10.7780/kjrs.2020.
Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R. and Stachniss, C. 2017. UAV-based crop and weed classification for smart farming. IEEE International Conference on Robotics and Automation (ICRA). 3024-3031. doi:10.1109/ICRA. 2017.7989347 10.1109/ICRA.2017.7989347
MAFRA. 2017. Ministry of Agriculture Food and Rural Affairs, main/ Accessed 26 June 2021.
MAFRA. 2021. Ministry of Agriculture, Food and Rural Affairs. Accessed 12 November 2021.
McNairn, H., Kross, A., Lapen, D., Caves, R. and Shang, J. 2014. Early season monitoring of corn and soybeans with Terra SAR-X and RADARSAT-2. Internati nal Journal of Applied Earth Observation and Geoinformation 28: 252-259. doi:10.1016/j.jag.2013.12.015 10.1016/j.jag.2013.12.015
Na, S.I., Park, C.W., So, K.H., Park, J.M. and Lee, K.D. 2017. Satellite Imagery based Winter Crop Classification Mapping using Hierarchica Classification. Korean Journal of Remote Sensing 33(5-2): 677-687. (in Korean) doi: 10.7780/kjrs.2017.
NICS. 2021. National Institute of Crop Science. Accessed 8 June 2021.
Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems 9(1): 62-66. doi: 10.1109/TSMC.1979.4310076 10.1109/TSMC.1979.4310076
Park, J.K. and Park, J.H. 2015. Crop classification using imagery of unmanned aerial vehicle. Journal of the Korean Society of Agricultural Engineers 57(6): 91-97. (in Korean) doi:10.5389/KSAE.2015.57.6.091 10.5389/KSAE.2015.57.6.091
Park, J.K. and Park, J.H. 2016. Applicability Evaluation of Agricultural Subsidies Inspection Using Unmanned Aerial Vehicle. Journal of the Korean Society of Agricultural Engineers 58(5): 29-37. (in Korean) doi:10.5389/ KSAE.2016.58.5.029 10.5389/KSAE.2016.58.5.029
Python 3.7, 2018, Accessed 2 July 2021.
Ronneberger, O., Fischer, P. and Brox, T. 2015. U-Net : Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer- Assisted Intervention(MICCAI) 9351: 234-241. arxiv: 1505.04597.pdf 10.1007/978-3-319-24574-4_28
Sameen, M.I., Pradhan, B. and Ziz, O.S. 2018. Classification of very high resolution aerial photos using spectral-spatial convolutional neural networks. Journal of Sensors 2018: 1-12. doi:10.1155/2018/7195432 10.1155/2018/7195432
Schlemper J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B. and Rueckert, D. 2019. Attention Gated Networks : Learning to Leverage Salient Regions in Medical Images. Medical Image Analysis 53: 197-207. doi:10.1016/ 10.1016/
Seong, S.K., Na, S.I. and Choi, J.W. 2020. Assessment of the FC-DenseNet for Crop Cultivation Area Extraction by Using RapidEye Satellite Imagery. Korean Journal of Remote Sensing 36(5-1): 823-833. (in Korean) doi:10.7780/ kjrs.2020.
Tensorflow. An End-to-End Open Source Machine Learning Platform. Accessed 12 August 2021.
Tong, X., Sun, B., Wei, J., Zuo, Z. and Su, S. 2021. EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection. Remote Sensing, 13(16): 3200. doi:10.3390/rs13163200 10.3390/rs13163200
Ulmas, P. and Liiv, I. 2016. Segmentation of satellite imagery using u-net models for land cover classification. IEEE Access 4: 1-11. arxiv:2003.02899.pdf
Yang, M.D., Huang, K.S., Kuo, Y.H., Tsai, H.P. and Lin, L.M. 2017. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery. Remote Sensing 9(6): 1-19. doi:10.3390/rs9060583 10.3390/rs9060583
Zhang, Z., Liu, Q. and Wang, Y. 2018. Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters 15(5): 749-753. doi: 10.1109/LGRS. 2018.2802944 10.1109/LGRS.2018.2802944
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 8
  • No :4
  • Pages :253-265
  • Received Date :2021. 12. 01
  • Revised Date :2021. 12. 09
  • Accepted Date : 2021. 12. 16