Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Applicability of Image Classification Using Deep Learning in Small Area : Case of Agricultural Lands Using UAV Image

딥러닝을 이용한 소규모 지역의 영상분류 적용성 분석 : UAV 영상을 이용한 농경지를 대상으로

  • Received : 2020.02.01
  • Accepted : 2020.02.25
  • Published : 2020.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

최근 UAV (Unmanned Aerial Vehicle)를 이용하여 고해상도 영상을 편리하게 취득할 수 있게 되면서 저비용으로 소규모 지역의 관측 및 공간정보 제작이 가능하게 되었다. 특히, 농업환경 모니터링을 위하여 작물생산 지역의 피복지도 생성에 대한 연구가 활발히 진행되고 있으며, 랜덤 포레스트와 SVM (Support Vector Machine) 및 CNN(Convolutional Neural Network) 을 적용하여 분류 성능을 비교한 결과 영상분류에서 딥러닝 적용에 대하여 활용도가 높은 것으로 나타났다. 특히, 위성영상을 이용한 피복분류는 위성영상 데이터 셋과 선행 파라메터를 사용하여 피복분류의 정확도와 시간에 대한 장점을 가지고 있다. 하지만, 무인항공기 영상은 위성영상과 공간해상도와 같은 특성이 달라 이를 적용하기에는 어려움이 있다. 이러한 문제점을 해결하기 위하여 위성영상 데이터 셋이 아닌 UAV를 이용한 데이터 셋과 국내의 소규모 복합 피복이 존재하는 농경지 분석에 활용이 가능한 딥러닝 알고리즘 적용 연구를 수행하였다. 본 연구에서는 최신 딥러닝의 의미론적 영상분류인 DeepLab V3+, FC-DenseNet (Fully Convolutional DenseNets), FRRN-B (Full-Resolution Residual Networks) 를 UAV 데이터 셋에 적용하여 영상분류를 수행하였다. 분류 결과 DeepLab V3+와 FC-DenseNet의 적용 결과가 기존 감독분류보다 높은 전체 정확도 97%, Kappa 계수 0.92로 소규모 지역의 UAV 영상을 활용한 피복분류의 적용가능성을 보여주었다.

Keywords

References

  1. Castro, J.D.B., Feitosa, R.Q., Rosa, L.C.L., Diaz, P.M.A., and Sanches, I.D.A. (2017), A comparative analysis of deep learning techniques for sub-tropical crop types recognition from multitemporal optical/SAR image sequences, Proceedings of 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images, IEEE, 17-20 october, Niteroi, Brazil, pp. 382-389.
  2. Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018), Encoder-decoder with atrous separable convolution for semantic image segmentation, In Proceedings of the European conference on computer vision (ECCV), pp. 82-92.
  3. Dang, L.M., Hassan, S.I., Suhyeon, I., kumar Sangaiah, A., Mehmood, I., Rho, S., Seo, S.H., and Moon, H. (2018), UAV based wilt detection system via convolutional neural networks. Sustainable Computing, pp.1-20.
  4. Fisher, P.F., Comber, A.J., and Wadsworth, R. (2005), Land Use and Land Cover: Contradiction or Complement, Re-Presenting GIS, Wiley, Chichester, pp. 85-98.
  5. Gao, Q., Lim, S., & Jia, X. (2018), Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sensing, Vol. 10, No. 2, pp.1-18. https://doi.org/10.3390/rs10010001
  6. Hall, O., Dahlin, S., Marstorp, H., Archila Bustos, M.F., Oborn, I., and Jirström, M. (2018), Classification of maize in complex smallholder farming systems using UAV imagery, Drones, Vol. 2, No. 3, pp.1-8.
  7. Huang, G., Liu, S., Van der Maaten, L., and Weinberger, K. Q. (2018), Condensenet: An efficient densenet using learned group convolutions, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2752-2761.
  8. Ishida, T., Kurihara, J., Viray, F.A., Namuco, S.B., Paringit, E.C., Perez, G.J., and Marciano Jr, J.J. (2018), A novel approach for vegetation classification using UAV-based hyperspectral imaging, Computers and Electronics in Agriculture, Vol.144, pp. 80-85. https://doi.org/10.1016/j.compag.2017.11.027
  9. Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017), The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation, In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 11-19.
  10. Jhonnerie, R., Siregar, V. P., and Nababan, B. (2017), Comparison of random forest algorithm which implemented on object and pixel based classification for mangrove land cover mapping, ICST 2016, Vol.1, pp. 292-302.
  11. Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018), 3D convolutional neural networks for crop classification with multi-temporal remote sensing images, Remote Sensing, Vol. 10, No. 1, pp. 1-17.
  12. Joe, W., Lim, Y., and Park. K.H, (2019), Deep learning based Land Cover Classification Using Convolutional Neural Network: a case study of Korea, Journal of the Korean Geographical Society, Vol. 54, No. 1, pp. 1-16. (in Korean with English abstract)
  13. Kamilaris, A. and Prenafeta-Boldu, F.X. (2018), Deep learning in agriculture: A survey, Computers and Electronics in Agriculture, Vol. 147, pp. 70-90. https://doi.org/10.1016/j.compag.2018.02.016
  14. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014), Large-scale video classification with convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725-1732.
  15. 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, Vol. 14, No. 5, pp. 778-782. https://doi.org/10.1109/LGRS.2017.2681128
  16. Kwak, G.H., Park, S., Yoo, H.Y., and Park, N.W. (2017), Updating land cover maps using object segmentation and past land cover information, Korean Journal of Remote Sensing, Vol. 33, No. 6_2, pp. 1089-1100. (in Korean with English abstract) https://doi.org/10.7780/KJRS.2017.33.6.2.5
  17. LeCun, Y., Bengio, Y., and Hinton, G. (2015), Deep learning, Nature, Vol. 521, pp. 436-444. https://doi.org/10.1038/nature14539
  18. Long, J., Shelhamer, E., and Darrell, T. (2015), Fully convolutional networks for semantic segmentation, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440.
  19. Murugan, D., Garg, A., and Singh, D. (2017), Development of an adaptive approach for precision agriculture monitoring with drone and satellite data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 12, pp. 5322-5328. https://doi.org/10.1109/JSTARS.2017.2746185
  20. Onojeghuo, A.O., Blackburn, G.A., Wang, Q., Atkinson, P.M., Kindred, D., and Miao, Y. (2018), Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data, International Journal of Remote Sensing, Vol. 39, No. 4, pp. 1042-1067. https://doi.org/10.1080/01431161.2017.1395969
  21. Park, J.K. and Park, J.H. (2015), Crops classification using imagery of unmanned aerial vehicle (UAV), Journal of the Korean Society of Agricultural Engineers, Vol. 57, No. 6, pp. 91-97. (in Korean with English abstract) https://doi.org/10.5389/KSAE.2015.57.6.091
  22. Pohlen, T., Hermans, A., Mathias, M., and Leibe, B. (2017), Full-resolution residual networks for semantic segmentation in street scenes, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4151-4160.
  23. Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., and Davis, C. H. (2017), Training deep convolutional neural networks for land-cover classification of high-resolution imagery, IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 4, pp. 549-553. https://doi.org/10.1109/LGRS.2017.2657778
  24. Skakun, S., Franch, B., Vermote, E., Roger, J.C., Becker-Reshef, I., Justice, C., and Kussul, N. (2017), Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model, Remote Sensing of Environment, Vol. 195, pp. 244-258. https://doi.org/10.1016/j.rse.2017.04.026
  25. Song, A. and Kim, Y. (2017), Deep learning-based hyperspectral image classification with application to environmental geographic information systems, Korean Journal of Remote Sensing, Vol. 33, No. 6, pp. 1061-1073. (in Korean with English abstract)
  26. Sung, S.M. and Lee, J.O. (2016), Accuracy of parcel boundary demarcation in agricultural area using UAV-photogrammetry, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 34, No. 1, pp. 53-62. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2016.34.1.53
  27. Torbick, N., Huang, X., Ziniti, B., Johnson, D., Masek, J., and Reba, M. (2018), Fusion of moderate resolution earth observations for operational crop type mapping, Remote Sensing, Vol. 10, No. 7, pp. 1-16.
  28. Xu, X., Ji, X., Jiang, J., Yao, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q., Yang, H., Shi, Z., and Cheng, T. (2018), Evaluation of one-class support vector classification for mapping the paddy rice planting area in Jiangsu Province of China from Landsat 8 OLI imagery, Remote Sensing, Vol. 10, No. 4, pp. 1-23.
  29. Zhong, Y., Fei, F., Liu, Y., Zhao, B. and Jiao, Jial. H. (2017), SatCNN: satellite image dataset classification using agile convolutional neural networks, Remote Sensing Letters, Vol. 8, No. 2, pp. 136-145. https://doi.org/10.1080/2150704X.2016.1235299
  30. Zhong, L., Gong, P., and Biging, G.S. (2014), Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery, Remote Sensing of Environment, Vol. 140, pp. 1-13. https://doi.org/10.1016/j.rse.2013.08.023

Cited by

  1. 기계학습 기법에 따른 KOMPSAT-3A 시가화 영상 분류 - 서울시 양재 지역을 중심으로 - vol.36, pp.6, 2020, https://doi.org/10.7780/kjrs.2020.36.6.2.7
  2. 무인항공기와 딥러닝(UNet)을 이용한 소규모 농지의 밭작물 분류 vol.38, pp.6, 2020, https://doi.org/10.7848/ksgpc.2020.38.6.671
  3. Hue 채널 영상의 다중 클래스 결합을 이용한 객체 기반 영상 분류 vol.37, pp.6, 2021, https://doi.org/10.7780/kjrs.2021.37.6.3.9