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Research Article

Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery

ORCID Icon, , , , &
Pages 3446-3479 | Received 15 Aug 2019, Accepted 19 Oct 2019, Published online: 06 Jan 2020

References

  • Adams, A., J. Baek, and M. A. Davis. 2010. “Fast High‐Dimensional Filtering Using the Permutohedral Lattice.” Computer Graphics Forum 29 (2): 753–762. doi:10.1111/j.1467-8659.2009.01645.x.
  • Albetis, J., S. Duthoit, F. Guttler, A. Jacquin, M. Goulard, H. Poilvé, J.-B. Féret, et al. 2017. “Detection of Flavescence Dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery.” Remote Sensing 9 (4): 308. doi:10.3390/rs9040308.
  • Anderson, G. L., J. H. Everitt, A. J. Richardson, and D. E. Escobar. 1993. “Using Satellite Data to Map False Broomweed (Ericameria Austrotexana) Infestations on South Texas Rangelands.” Weed Technology 7 (4): 865–871. doi:10.1017/S0890037X00037908.
  • Baatz, M., and A. Schäpe. 2000. “Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation.” Angewandte Geographische Informationsverarbeitung XII, Karlsruhe, Germany, WichmannVerlag.
  • Belgiu, M., and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114: 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
  • Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
  • Boureau, Y. L., F. Bach, Y. Lecun, and J. Ponce. 2010. “Learning Mid-level Features for Recognition.” Computer Vision & Pattern Recognition, San Francisco, CA, USA.
  • Breiman, L. 2001. “Random Forests.”
  • Chapelle, O., P. Haffner, and V. N. Vapnik. 1999. “Support Vector Machines for Histogram-based Image Classification.” IEEE Transactions on Neural Networks 10 (5): 1055–1064. doi:10.1109/72.788646.
  • Chen, L. C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2018. “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–848. doi:10.1109/TPAMI.2017.2699184.
  • Clewley, D., P. Bunting, J. Shepherd, S. Gillingham, N. Flood, J. Dymond, R. Lucas, et al. 2014. “A Python-based Open Source System for Geographic Object-based Image Analysis (Geobia) Utilizing Raster Attribute Tables.” Remote Sensing 6 (7): 6111–6135. doi:10.3390/rs6076111.
  • Csurka, G., D. Larlus, and F. Perronnin. 2013. “What Is a Good Evaluation Measure for Semantic Segmentation.” BMVC, Bristol, UK.
  • de Castro, A., J. Torres-Sánchez, J. Peña, F. Jiménez-Brenes, O. Csillik, and F. López-Granados. 2018. “An Automatic Random Forest-obia Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery.” Remote Sensing 10 (3): 285. doi:10.3390/rs10020285.
  • Ding, S., C. Su, and J. Yu. 2011. “An Optimizing Bp Neural Network Algorithm Based on Genetic Algorithm.” Artificial Intelligence Review 36 (2): 153–162. doi:10.1007/s10462-011-9208-z.
  • Donahue, J., Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. 2013. “Decaf: A Deep Convolutional Activation Feature for Generic Visual Recognition.” International Conference on Machine Learning, Atlanta, USA.
  • Gamanya, R., P. De Maeyer, and M. De Dapper. 2009. “Object-oriented Change Detection for the City of Harare, Zimbabwe.” Expert Systems with Applications 36 (1): 571–588. doi:10.1016/j.eswa.2007.09.067.
  • Gil-Yepes, J. L., L. A. Ruiz, J. A. Recio, Á. Balaguer-Beser, and T. Hermosilla. 2016. “Description and Validation of a New Set of Object-based Temporal Geostatistical Features for Land-use/land-cover Change Detection.” ISPRS Journal of Photogrammetry and Remote Sensing 121: 77–91. doi:10.1016/j.isprsjprs.2016.08.010.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Las Vegas, NV, USA.
  • Hu, F., G. S. Xia, J. Hu, and L. Zhang. 2015. “Transferring Deep Convolutional Neural Networks for the Scene Classification of High-resolution Remote Sensing Imagery.” Remote Sensing 7 (11): 14680–14707. doi:10.3390/rs71114680.
  • Huang, H., J. Deng, Y. Lan, A. Yang, X. Deng, and L. Zhang. 2018a. “A Fully Convolutional Network for Weed Mapping of Unmanned Aerial Vehicle (UAV) Imagery.” PLoS One 13 (4): e196302.
  • Huang, H., J. Deng, Y. Lan, A. Yang, X. Deng, L. Zhang, S. Wen, Y. Jiang, G. Suo, and P. Chen 2018b. “A Two-stage Classification Approach for the Detection of Spider Mite- Infested Cotton Using UAV Multispectral Imagery.” Remote Sensing Letters 9 (10): 933–941. doi:10.1080/2150704X.2018.1498600.
  • Huang, H., Y. Lan, J. Deng, A. Yang, X. Deng, L. Zhang, and S. Wen. 2018c. “A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery.” Sensors 18 (7): 2113. doi:10.3390/s18072113.
  • Jiang, S. J., and S. Qiang. 2005. “The Effect of the Mycotoxin of α,β-dehydrocurvularin from Curvularia Eragrostidis on Ps Ii in Digitaria Sanguinalis.” Scientia Agricultura Sinica38(7): 1373-1378.
  • Johnson, B., and Z. Xie. 2011. “Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-scale Approach.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (4): 473–483. doi:10.1016/j.isprsjprs.2011.02.006.
  • Krähenbühl, P., and V. Koltun. 2012. “Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials.” arXiv:1210.5644.
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” International Conference on Neural Information Processing Systems, Curran Associates. .
  • Längkvist, M., A. Kiselev, M. Alirezaie, and A. Loutfi. 2016. “Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks.” Remote Sensing 8 (4): 329. doi:10.3390/rs8040329.
  • Lecun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539.
  • Li, X., B. Qi, and W. Lu. 2009. “A New Improved BP Neural Network Algorithm.” International Conference on Intelligent Computation Technology & Automation, Changsha, Hunan, China.
  • Liu, Y., H. Wang, T. Guo, J. Zhang, X. Tang, and Z. Chen 2013. “Breeding and Application of High-quality and Diseaseresistant Rice Variety.” Guangdong Agricultural Sciences 10: 8–11.
  • López-Granados, F., J. Torres-Sánchez, A. Serrano-Pérez, A. I. de Castro, F.-J. Mesas-Carrascosa, and J.-M. Peña. 2016. “Early Season Weed Mapping in Sunflower Using UAV Technology: Variability of Herbicide Treatment Maps against Weed Thresholds.” Precision Agriculture 17 (2): 183–199. doi:10.1007/s11119-015-9415-8.
  • Lottes, P., R. Khanna, J. Pfeifer, R. Siegwart, and C. Stachniss. 2017. “UAV-based Crop and Weed Classification for Smart Farming.” IEEE International Conference on Robotics & Automation, IEEE, Singapore.
  • Macqueen, J. 1965. “Some Methods for Classification and Analysis of Multivariate Observations.” Proc of Berkeley Symposium on Mathematical Statistics & Probability. University of California, Berkeley, USA.
  • Martín, M. P., L. Barreto, and C. Fernández-Quintanilla. 2011. “Discrimination of Sterile Oat (Avena sterilis) in Winter Barley (Hordeum vulgare) Using Quickbird Satellite Images.” Crop Protection 30 (10): 1363–1369. doi:10.1016/j.cropro.2011.06.008.
  • Mitich, L. W. 1990. “Barnyardgrass.” Weed Technology: A Journal of the Weed Science Society of America 4 (4): 918–920.
  • Ojala, T., and I. Harwood. 1996. “A Comparative Study of Texture Measures with Classification Based on Feature Distributions.” Pattern Recognition 29 (1): 51–59. doi:10.1016/0031-3203(95)00067-4.
  • Oquab, M., L. Bottou, I. Laptev, and J. Sivic. 2014. “Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks.” Computer Vision and Pattern Recognition, IEEE, Columbus, Ohio, USA
  • Pantazi, X. E., A. A. Tamouridou, T. K. Alexandridis, A. L. Lagopodi, J. Kashefi, and D. Moshou. 2017. “Evaluation of Hierarchical Self-organising Maps for Weed Mapping Using UAS Multispectral Imagery.” Computers and Electronics in Agriculture 139: 224–230. doi:10.1016/j.compag.2017.05.026.
  • Peña Barragán, J. M., F. López Granados, M. Jurado Expósito, and L. García‐Torres. 2007. “Mapping Ridolfia Segetum Patches in Sunflower Crop Using Remote Sensing.” Weed Research 47 (2): 164–172. doi:10.1111/j.1365-3180.2007.00553.x.
  • Peña JM, Torres-Sánchez J, de Castro AI, Kelly M, and López-Granados F. 2013. “Weed Mapping in Early-season Maize Fields Using Object-based Analysis of Unmanned Aerial Vehicle (UAV) Images.” PloS One 8 (10): e77151. doi:10.1371/journal.pone.0077151.
  • Sa, I., M. Popović, R. Khanna, Z. Chen, P. Lottes, F. Liebisch, and J. Nieto, et al. 2018. “Weedmap: A Large-scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming.” Remote Sensing 10 (9): 1423. doi:10.3390/rs10091423.
  • Sánchez, A. V. D. 2003. “Advanced Support Vector Machines and Kernel Methods.” Neurocomputing 55 (1–2): 5–20. doi:10.1016/S0925-2312(03)00373-4.
  • Schwartz, A. M., S. M. Paskewitz, A. P. Orth, M. J. Tesch, I. Y. Toong, and W. G. Goodman 1998. “The Lethal Effects of Cyperus iria on Aedes aegypti.” Journal of the American Mosquito Control Association 14 (1): 78.
  • Scikit-Learn. 2019 February 18. https://scikit-learn.org/stable/modules/ensemble.html#forest
  • Sharma, A., X. Liu, X. Yang, and D. Shi. 2017. “A Patch-based Convolutional Neural Network for Remote Sensing Image Classification.” Neural Networks 95: 19–28. doi:10.1016/j.neunet.2017.07.017.
  • Shelhamer, E., J. Long, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” CVPR, Boston, Massachusetts, USA.
  • Shepherd, J., P. Bunting, and J. Dymond. 2019. “Operational Large-scale Segmentation of Imagery Based on Iterative Elimination.” Remote Sensing 11 (6): 658. doi:10.3390/rs11060658.
  • Simonyan, K., and A. Zisserman. 2015. “Very Deep Convolutional Networks for Large-scale Image Recognition.” ICLR, San Diego, CA, USA.
  • Teichmann, M. T. T., and R. Cipolla. 2018. “Convolutional Crfs for Semantic Segmentatio.” arXiv:180504777.
  • Torres-Sánchez, J., F. López-Granados, A. I. De Castro, and J. M. Peña-Barragán. 2013. “Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management.” PloS One 8 (3): e58210. doi:10.1371/journal.pone.0058210.
  • Vapnik, V. N. 1999. “An Overview of Statistical Learning Theory.” IEEE Transactions on Neural Networks 10 (5): 988–999. doi:10.1109/72.788640.
  • Yu, J., H. Gao, L. Pan, Z. Yao, and L. Dong. 2017. “Mechanism of Resistance to Cyhalofop-butyl in Chinese Sprangletop (Leptochloa chinensis (L.) Nees).” Pesticide Biochemistry and Physiology 143: 306–311. doi:10.1016/j.pestbp.2016.11.001.

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