Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series

References

• Audebert, N., A. Boulch, H. Randrianarivo, B. Le Saux, M. Ferecatu, S. Lefevre, and R. Marlet. 2017. “Deep Learning for Urban Remote Sensing.” Paper presented at the Joint Urban Remote Sensing Event (JURSE).
   Google Scholar
• Badrinarayanan, V., A. Kendall, and R. Cipolla. 2017. “Segnet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12): 2481–2495. doi:10.1109/TPAMI.2016.2644615.
   PubMed Web of Science ®Google Scholar
• Blaschke, T., G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Q. Feitosa, et al. 2014. “Geographic Object-Based Image Analysis – Towards a New Paradigm.” ISPRS Journal of Photogrammetry and Remote Sensing 87: 180–191. doi:10.1016/j.isprsjprs.2013.09.014.
   PubMed Web of Science ®Google Scholar
• Boryan, C., Z. Yang, R. Mueller, and M. Craig. 2011. “Monitoring US Agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program.” Geocarto International 26 (5): 341–358. doi:10.1080/10106049.2011.562309.
   Web of Science ®Google Scholar
• Breuel, T. M. 2015. “Benchmarking of LSTM Networks.” arXiv preprint arXiv:1508.02774.
   Google Scholar
• Burnett, M., B. Weinstein, and A. Mitchell. 2007. “ECHO–Enabling Interoperability with NASA Earth Science Data and Services.” Paper presented at the Geoscience and Remote Sensing Symposium, 2007, IGARSS 2007, IEEE International.
   Google Scholar
• Canty, M. J. 2006. Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL. Boca Raton, FL: CRC Press.
   Google Scholar
• Computing, Hyspeed. 2013. “Big Data and Remote Sensing – Where Does All This Imagery Fit into the Picture?”, Accessed May 16 2016. https://hyspeedblog.wordpress.com/2013/03/22/big-data-and-remote-sensing-where-does-all-this-imagery-fit-into-the-picture/
   Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Coon, R. C., and F. L. Leistritz. 2010. The Role of Agriculture in the North Dakota Economy. Fargo, ND: Department of Agribusiness & Applied Economics, North Dakota State University.
   Google Scholar
• Cooner, A. J., Y. Shao, and J. B. Campbell. 2016. “Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake.” Remote Sensing 8 (10): 868. doi:10.3390/rs8100868.
   Web of Science ®Google Scholar
• Dahl, G. E., T. N. Sainath, and G. E. Hinton. 2013. “Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout.” Paper presented at the Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on.
   Google Scholar
• Das, M., and S. K. Ghosh. 2016. “Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data.” IEEE Geoscience and Remote Sensing Letters 13 (12): 1984–1988. doi:10.1109/LGRS.2016.2619984.
   Web of Science ®Google Scholar
• Duro, D. C., S. E. Franklin, and M. G. Dubé. 2012. “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118: 259–272. doi:10.1016/j.rse.2011.11.020.
   Web of Science ®Google Scholar
• Gers, F. A., and J. Schmidhuber. 2000. “Recurrent Nets that Time and Count.” Paper presented at the ijcnn.
   Google Scholar
• Glorot, X., and Y. Bengio. 2010. “Understanding the Difficulty of Training Deep Feedforward Neural Networks.” Paper presented at the Aistats.
   Google Scholar
• Graves, A., A.-R. Mohamed, and G. Hinton. 2013. “Speech Recognition with Deep Recurrent Neural Networks.” Paper presented at the Acoustics, speech and signal processing (icassp), 2013 ieee international conference on.
   Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches.” ISPRS Journal of Photogrammetry and Remote Sensing 80: 91–106. doi:10.1016/j.isprsjprs.2013.03.006.
   Web of Science ®Google Scholar
• Ienco, D., R. Gaetano, C. Dupaquier, and P. Maurel. 2017. “Land Cover Classification via Multi-Temporal Spatial Data by Recurrent Neural Networks.” arXiv preprint arXiv:04055.
   Google Scholar
• Irons, J. R., J. L. Dwyer, and J. A. Barsi. 2012. “The Next Landsat Satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment 122: 11–21. doi:10.1016/j.rse.2011.08.026.
   Web of Science ®Google Scholar
• Jantzi, D., K. Hagemeister, K. F. Brenda Krupich, C. B. Grafton, D. Goehring, S. Perdue, and H. Hamer. 2017. “North Dakota Agricultural Statistics 2017.” Ag Statistics, USDA NASS.
   Google Scholar
• Jozefowicz, R., W. Zaremba, and I. Sutskever. 2015. “An Empirical Exploration of Recurrent Network Architectures.” Paper presented at the International Conference on Machine Learning.
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Paper presented at the Advances in neural information processing systems.
   Google Scholar
• Kussul, N., M. Lavreniuk, S. Skakun, and A. Shelestov. 2017. “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data.” IEEE Geoscience and Remote Sensing Letters 14: 778–782. doi:10.1109/LGRS.2017.2681128.
   Web of Science ®Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Li, W., H. Fu, L. Yu, and A. Cracknell. 2016. “Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images.” Remote Sensing 9 (1): 22. doi:10.3390/rs9010022.
   Web of Science ®Google Scholar
• Long, J., E. Shelhamer, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
   Google Scholar
• Ma, Y., H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, and W. Jie. 2015. “Remote Sensing Big Data Computing: Challenges and Opportunities.” Future Generation Computer Systems 51: 47–60. doi:10.1016/j.future.2014.10.029.
   Web of Science ®Google Scholar
• Maggiori, E., Y. Tarabalka, G. Charpiat, and P. Alliez. 2017. “Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (2): 645–657. doi:10.1109/TGRS.2016.2612821.
   Web of Science ®Google Scholar
• Marmanis, D., M. Datcu, T. Esch, and U. Stilla. 2016. “Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks.” IEEE Geoscience and Remote Sensing Letters 13 (1): 105–109. doi:10.1109/LGRS.2015.2499239.
   Web of Science ®Google Scholar
• Merity, S., N. S. Keskar, and R. Socher. 2017. “Regularizing and Optimizing LSTM Language Models.”arXiv preprint arXiv:1708.02182.
   Google Scholar
• Nair, V., and G. E. Hinton. 2010. “Rectified Linear Units Improve Restricted Boltzmann Machines.” Paper presented at the Proceedings of the 27th international conference on machine learning (ICML–10).
   Google Scholar
• NASA. 2016. “An Overview of EOSDIS.” Accessed May 16 2016. httpsL–10://earthdata.nasa.gov/about
   Google Scholar
• Roy, D. P., M. A. Wulder, T. R. Loveland, C. E. Woodcock, R. G. Allen, M. C. Anderson, D. Helder, J. R. Irons, D. M. Johnson, and R. Kennedy. 2014. “Landsat-8: Science and Product Vision for Terrestrial Global Change Research.” Remote Sensing of Environment 145: 154–172. doi:10.1016/j.rse.2014.02.001.
   Web of Science ®Google Scholar
• Schaffer, C. 1993. “Overfitting Avoidance as Bias.” Machine Learning 10 (2): 153–178. doi:10.1007/BF00993504.
   Web of Science ®Google Scholar
• Schuster, M., and K. K. Paliwal. 1997. “Bidirectional Recurrent Neural Networks.” IEEE Transactions on Signal Processing 45 (11): 2673–2681. doi:10.1109/78.650093.
   Web of Science ®Google Scholar
• Senior, A., G. Heigold, and K. Yang. 2013. “An Empirical Study of Learning Rates in Deep Neural Networks for Speech Recognition.” Paper presented at the Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on.
   Google Scholar
• Songnian, L., S. Dragicevic, F. A. Castro, M. Sester, S. Winter, A. Coltekin, C. Pettit, et al. 2016. “Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges.” ISPRS Journal of Photogrammetry and Remote Sensing 115: 119–133. doi:10.1016/j.isprsjprs.2015.10.012.
   Web of Science ®Google Scholar
• Srivastava, N., G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research 15 (1): 1929–1958.
   Google Scholar
• Sun, Z., C. Peng, M. Deng, A. Chen, P. Yue, H. Fang, and L. Di. 2014. “Automation of Customized and Near-Real-Time Vegetation Condition Index Generation through Cyberinfrastructure-Based Geoprocessing Workflows.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (11): 4512–4522. doi:10.1109/jstars.2014.2377248..
   Web of Science ®Google Scholar
• Sun, Z., H. Fang, M. Deng, A. Chen, P. Yue, and L. Di. 2015. “Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects from Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 53 (7): 3737–3748. doi:10.1109/TGRS.2014.2382566..
   Web of Science ®Google Scholar
• Sun, Z., H. Fang, L. Di, and P. Yue. 2016. “Realizing Parameterless Automatic Classification of Remote Sensing Imagery Using Ontology Engineering and Cyberinfrastructure Techniques.” Computers & Geosciences 94: 56–67. doi:10.1016/j.cageo.2016.06.004.
   Web of Science ®Google Scholar
• USGS. 2018. “Landsat Project Statistics.” Accessed July 3 2018. http://landsat.usgs.gov/Landsat_Project_Statistics.php
   Google Scholar
• Ward, K. 2008. “NASA Earth Observations (NEO): Data Imagery for Education and Visualization.” Paper presented at the AGU Fall Meeting Abstracts.
   Google Scholar
• Wikipedia. 2014. “Landsat Program.” Accessed September 21 2014. http://en.wikipedia.org/wiki/Landsat_program
   Google Scholar
• Zhao, W., Z. Guo, J. Yue, X. Zhang, and L. Luo. 2015. “On Combining Multiscale Deep Learning Features for the Classification of Hyperspectral Remote Sensing Imagery.” International Journal of Remote Sensing 36 (13): 3368–3379. doi:10.1080/2150704X.2015.1062157.
   Web of Science ®Google Scholar
• Zibulevsky, M., and M. Elad. 2010. “L1-L2 Optimization in Signal and Image Processing.” IEEE Signal Processing Magazine 27 (3): 76–88. doi:10.1109/MSP.2010.936023.
   Web of Science ®Google Scholar