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2021 Vol.8, Issue 4 Preview Page

Original Article

31 December 2021. pp. 253-265
Abstract
References
1
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
2
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
3
Keras. The Python Deep Learning API. https://keras.io/. Accessed 12 August 2021.
4
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
5
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
6
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
7
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
8
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. 36.6.2.9
9
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
10
MAFRA. 2017. Ministry of Agriculture Food and Rural Affairs, https://agis.epis.or.kr/ASD/ main/intro.do#. Accessed 26 June 2021.
11
MAFRA. 2021. Ministry of Agriculture, Food and Rural Affairs. https://lib.mafra.go.kr/skyblueimage/5622.pdf. Accessed 12 November 2021.
12
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
13
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.2017.33.5.2.7
14
NICS. 2021. National Institute of Crop Science. https://nongupin.co.kr/news/articleView.html?idxno=92916. Accessed 8 June 2021.
15
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
16
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
17
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
18
Python 3.7, 2018, https://python.org/. Accessed 2 July 2021.
19
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
20
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
21
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/j.media.2019.01.012 10.1016/j.media.2019.01.01230802813PMC7610718
22
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.2020.36.5.1.14
23
Tensorflow. An End-to-End Open Source Machine Learning Platform. https://tensorflow.org/. Accessed 12 August 2021.
24
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
25
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
26
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
27
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
Information
  • 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