Classification of Summer Crops Using Active Learning Techniques on Landsat Images in the Northwest of the Province of Buenos Aires

[1]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[2]  Melba M. Crawford,et al.  Active Learning: Any Value for Classification of Remotely Sensed Data? , 2013, Proceedings of the IEEE.

[3]  Waterloo Pereira Filho,et al.  Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids , 2019, Journal of Applied Remote Sensing.

[4]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[5]  Dawei Zheng.  Future Information Engineering and Manufacturing Science: Proceedings of the 2014 International Conference on Future Information Engineering and Manufacturing Science (FIEMS 2014), June 26-27, 2014, Beijing, China , 2015 .

[6]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[7]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[8]  Fábio Augusto Faria,et al.  Active Learning Approaches for Deforested Area Classification , 2018, 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

[9]  Yonghui Wang,et al.  Agriculture robot and applications , 2015 .

[10]  Mikhail F. Kanevski,et al.  A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[11]  Paul Scheunders,et al.  Contributions of machine learning to remote sensing data analysis , 2018 .

[12]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[13]  Min Bai,et al.  Learning Deep Structured Active Contours End-to-End , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Savvas Dimitriadis,et al.  Applying Machine Learning to Extract New Knowledge in Precision Agriculture Applications , 2008, 2008 Panhellenic Conference on Informatics.

[15]  Juan Frausto-Solís,et al.  Predictive ability of machine learning methods for massive crop yield prediction , 2014 .

[16]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[17]  Hyungmin Kim,et al.  An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode , 2017, Sensors.

[18]  S. Veenadhari,et al.  Machine learning approach for forecasting crop yield based on climatic parameters , 2014, 2014 International Conference on Computer Communication and Informatics.

[19]  Xiaoyu Chang,et al.  A label-noise robust active learning sample collection method for multi-temporal urban land-cover classification and change analysis , 2020 .

[20]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[21]  Peng Liu,et al.  Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 Active Deep Learning for Classification of Hyperspectral Images , 2022 .

[22]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[23]  Gustavo Camps-Valls,et al.  Machine learning in remote sensing data processing , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

[24]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[25]  Matthew C. Hansen,et al.  National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey , 2017 .

[26]  Andrea S. Brendel,et al.  Assessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data , 2019, Journal of Applied Remote Sensing.

[27]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[28]  J. Maynard,et al.  Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability , 2017 .

[29]  Mansi S. Subhedar,et al.  Land Cover Classification using Support Vector Machine , 2015 .

[30]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.