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The aim of this paper is to map agricultural crops by classifying satellite image time series.
Hierarchical text classification with reinforced label assignment
Mao, Y., Tian, J., Han, J., Ren, X., 2019 · 1908
Earlier work this paper cites.
Learning with hierarchical complement objective
Chen, H.Y., Tsai, L.H., Chang, S.C., Pan, J.Y., Chen, Y.T., Wei, W., Juan, D.C., 2019 · 1911
Earlier work this paper cites.
Crop-rotation structured classification using multi-source sentinel images and lpis for crop type mapping, in: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE. pp. 1950–1953
Bailly, S., Giordano, S., Landrieu, L., Chehata, N., 2018 · 1953
Earlier work this paper cites.
Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems
Stoian, A., Poulain, V., Inglada, J., Poughon, V., Derksen, D., 2019 · 1986
Earlier work this paper cites.
Hierarchical mapping of annual global land cover 2001 to present: The modis collection 6 land cover product
Sulla-Menashe, D., Gray, J.M., Abercrombie, S.P., Friedl, M.A., 2019 · 2001
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002 · 2002
Earlier work this paper cites.
Convolutional tensor-train lstm for spatio-temporal learning
Su, J., Byeon, W., Huang, F., Kautz, J., Anandkumar, A., 2020 · 2002
Earlier work this paper cites.
Classification of hyperspectral remote sensing images with support vector machines
Melgani, F., Bruzzone, L., 2004 · 2004
Earlier work this paper cites.
Phicnet: Physics-incorporated convolutional recurrent neural networks for modeling dynamical systems
Saha, P., Dash, S., Mukhopadhyay, S., 2020 · 2004
Earlier work this paper cites.
Environmental cross-compliance mitigates nitrogen and phosphorus pollution from Swiss agriculture
Herzog, F., Prasuhn, V., Spiess, E., Richner, W., 2008 · 2008
Earlier work this paper cites.
Large-area crop mapping using time-series modis 250 m ndvi data: An assessment for the us central great plains
Wardlow, B.D., Egbert, S.L., 2008 · 2008
Earlier work this paper cites.
Hierarchical object oriented classification using very high resolution imagery and lidar data over urban areas
Chen, Y., Su, W., Li, J., Sun, Z., 2009 · 2009
Earlier work this paper cites.
Conservation agriculture and smallholder farming in Africa: The heretics’ view
Giller, K.E., Witter, E., Corbeels, M., Tittonell, P., 2009 · 2009
Earlier work this paper cites.
Learning from imbalanced data
He, H., Garcia, E.A., 2009 · 2009
Earlier work this paper cites.
Per-field irrigated crop classification in arid central asia using spot and aster data
Conrad, C., Fritsch, S., Zeidler, J., Rücker, G., Dech, S., 2010 · 2010
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE. pp. 3485–3492
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A., 2010 · 2010
Earlier work this paper cites.
Nitrogen as a threat to European terrestrial biodiversity
Dise, N.B., Ashmore, M., Belyazid, S., Bleeker, A., Bobbink, R., Vries, W.D., Erisman, J.W., Spranger, T., Stevens, C.J., Berg, L.V.D., 2011 · 2011
Earlier work this paper cites.
Environmental impact of different agricultural management practices: Conventional vs. Organic agriculture
Gomiero, T., Pimentel, D., Paoletti, M.G., 2011 · 2011
Earlier work this paper cites.
Cost-sensitive learning and the class imbalance problem
Ling, C.X., Sheng, V.S., 2008 · 2011
Earlier work this paper cites.
Object-based crop identification using multiple vegetation indices, textural features and crop phenology
Peña-Barragán, J.M., Ngugi, M.K., Plant, R.E., Six, J., 2011 · 2011
Earlier work this paper cites.
Hierarchical mapping of northern eurasian land cover using modis data
Sulla-Menashe, D., Friedl, M.A., Krankina, O.N., Baccini, A., Woodcock, C.E., Sibley, A., Sun, G., Kharuk, V., Elsakov, V., 2011 · 2011
Earlier work this paper cites.
Policy reforms to promote efficient and sustainable water use in swiss agriculture
Finger, R., Lehmann, N., 2012 · 2012
Earlier work this paper cites.
Crop type mapping using spectral–temporal profiles and phenological information
Foerster, S., Kaden, K., Foerster, M., Itzerott, S., 2012 · 2012
Earlier work this paper cites.
On-farm effects of tillage and crops on soil erosion measured over 10 years in Switzerland
Prasuhn, V., 2012 · 2012
Earlier work this paper cites.
Agricultural expansion and its impacts on tropical nature
Laurance, W.F., Sayer, J., Cassman, K.G., 2014 · 2013
Earlier work this paper cites.
Selection of hyperspectral narrowbands (hnbs) and composition of hyperspectral twoband vegetation indices (HVIS) for biophysical characterization and discrimination of crop types using field reflectance and hyperion/EO-1 data
Thenkabail, P.S., Mariotto, I., Gumma, M.K., Middleton, E.M., Landis, D.R., Huemmrich, K.F., 2013 · 2013
Cited alongside, same era.
Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of uzbekistan using multitemporal rapideye images
Conrad, C., Dech, S., Dubovyk, O., Fritsch, S., Klein, D., Löw, F., Schorcht, G., Zeidler, J., 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization, in: ICLR
Kingma, D.P., Ba, J., 2014 · 2014
Cited alongside, same era.
Crop type classification using vegetation indices of rapideye imagery
Ustuner, M., Sanli, F.B., Abdikan, S., Esetlili, M., Kurucu, Y., 2014 · 2014
Cited alongside, same era.
Error-driven incremental learning in deep convolutional neural network for large-scale image classification, in: Proceedings of the ACM International Conference on Multimedia, pp. 177–186
Combined convolutional and recurrent neural networks for hierarchical classification of images
Koo, J., Klabjan, D., Utke, J., 2018 · 2018
Later among the works it cites.
Videolstm convolves, attends and flows for action recognition
Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., Snoek, C.G., 2018 · 2018
Later among the works it cites.
Learning to reweight examples for robust deep learning, in: ICML
Ren, M., Zeng, W., Yang, B., Urtasun, R., 2018 · 2018
Later among the works it cites.
How much does multi-temporal sentinel-2 data improve crop type classification?
Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., Ng, W.T., 2018 · 2018
Later among the works it cites.
Hierarchical multi-label classification networks, in: International Conference on Machine Learning, pp. 5075–5084
Wehrmann, J., Cerri, R., Barros, R., 2018 · 2018
Later among the works it cites.
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Xiao, T., Zhang, J., Yang, K., Peng, Y., Zhang, Z., 2014 · 2014
Cited alongside, same era.
Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery
Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D., Dedieu, G., Sepulcre, G., Bontemps, S., Defourny, P., et al., 2015 · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Cited alongside, same era.
A hidden markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data
Siachalou, S., Mallinis, G., Tsakiri-Strati, M., 2015 · 2015
Cited alongside, same era.
Plant phenotyping: from bean weighing to image analysis
Walter, A., Liebisch, F., Hund, A., 2015 · 2015
Cited alongside, same era.
Convolutional LSTM network: A machine learning approach for precipitation nowcasting, in: Advances in neural information processing systems
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c., 2015 · 2015
Cited alongside, same era.
Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 2740–2748
Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., Yu, Y., 2015 · 2015
Cited alongside, same era.
Optical remotely sensed time series data for land cover classification: A review
Gómez, C., White, J.C., Wulder, M.A., 2016 · 2016
Cited alongside, same era.
Class-balanced loss based on effective number of samples, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S., 2019 · 2019
Later among the works it cites.
Using a u-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across queensland, australia
Flood, N., Watson, F., Collett, L., 2019 · 2019
Later among the works it cites.
A hierarchical classification framework of satellite multispectral/hyperspectral images for mapping coastal wetlands
Jiao, L., Sun, W., Yang, G., Ren, G., Liu, Y., 2019 · 2019
Later among the works it cites.
Striking the right balance with uncertainty, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 103–112
Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L., 2019 · 2019
Later among the works it cites.
Temporal convolutional neural network for the classification of satellite image time series
Pelletier, C., Webb, G.I., Petitjean, F., 2019 · 2019
Later among the works it cites.
Breizhcrops: A satellite time series dataset for crop type identification, in: ICML Workshop
Rußwurm, M., Lefèvre, S., Körner, M., 2019 · 2019
Later among the works it cites.
Semantic segmentation of crop type in africa: A novel dataset and analysis of deep learning methods, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops
Rustowicz, R., Cheong, R., Wang, L., Ermon, S., Burke, M., Lobell, D., 2019 · 2019
Later among the works it cites.
Time-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series, in: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE. pp. 6247–6250
Sainte Fare Garnot, V., Landrieu, L., Giordano, S., Chehata, N., 2019 · 2019
Later among the works it cites.
Deep learning based multi-temporal crop classification
Zhong, L., Hu, L., Zhou, H., 2019 · 2019
Later among the works it cites.
Spectral vegetation indices to track senescence dynamics in diverse wheat germplasm
Anderegg, J., Yu, K., Aasen, H., Walter, A., Liebisch, F., Hund, A., 2020 · 2020
Later among the works it cites.
Landwirtschaft und Ernährung - Taschenstatistik 2020
Bundesamt für Statistik, 2020 · 2020
Later among the works it cites.
Hierarchical classification of sentinel 2-a images for land use and land cover mapping and its use for the corine system
Demirkan, D.Ç., Koz, A., Düzgün, H.S., 2020 · 2020
Later among the works it cites.
Tree-cnn: a hierarchical deep convolutional neural network for incremental learning
Roy, D., Panda, P., Roy, K., 2020 · 2020
Later among the works it cites.
Self-attention for raw optical satellite time series classification
Rußwurm, M., Körner, M., 2020 · 2020
Later among the works it cites.
Meta-learning for few-shot land cover classification, in: Proceedings of the ieee/cvf conference on computer vision and pattern recognition workshops, pp. 200–201
Rußwurm, M., Wang, S., Korner, M., Lobell, D., 2020 · 2020
Later among the works it cites.
Satellite image time series classification with pixel-set encoders and temporal self-attention, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12325–12334
Sainte Fare Garnot, V., Landrieu, L., Giordano, S., Chehata, N., 2020 · 2020
Later among the works it cites.
Spatial monitoring of grassland management using multi-temporal satellite imagery
Stumpf, F., Schneider, M.K., Keller, A., Mayr, A., Rentschler, T., Meuli, R.G., Schaepman, M., Liebisch, F., 2020 · 2020
Later among the works it cites.
Gating revisited: Deep multi-layer rnns that can be trained
Turkoglu, M.O., D’Aronco, S., Wegner, J., Schindler, K., 2021 · 2021
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Discriminative transfer learning with tree-based priors, in: Advances in neural information processing systems, pp. 2094–2102
Srivastava, N., Salakhutdinov, R.R., 2013 · 2094
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