Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation

References

• Audebert, N., B. Le Saux, and S. Lefevre. 2017. “Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps.” 67–75. http://openaccess.thecvf.com/content_cvpr_2017_workshops/w18/html/Audebert_Joint_Learning_From_CVPR_2017_paper.html
   Google Scholar
• Audebert, N., B. Le Saux, and S. Lefèvre. 2018. “Beyond RGB: Very High Resolution Urban Remote Sensing with Multimodal Deep Networks.” ISPRS Journal of Photogrammetry and Remote Sensing, Geospatial Computer Vision 140 (June): 20–32. doi:https://doi.org/10.1016/j.isprsjprs.2017.11.011.
   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:https://doi.org/10.1109/TPAMI.2016.2644615.
   PubMed Web of Science ®Google Scholar
• Ball, J. E., D. T. Anderson, and C. S. Chan. 2017. “Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools, and Challenges for the Community.” Journal of Applied Remote Sensing 11 (4): 042609. doi:https://doi.org/10.1117/1.JRS.11.042609.
   Web of Science ®Google Scholar
• Ball, J. E., D. T. Anderson, and P. Wei. 2018. “State-of-the-Art and Gaps for Deep Learning on Limited Training Data in Remote Sensing.” ArXiv Preprint. ArXiv:1807.11573.
   Google Scholar
• Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259. doi:https://doi.org/10.1016/j.neunet.2018.07.011
   PubMed Web of Science ®Google Scholar
• Caliskan, A., M. E. Yuksel, H. Badem, and A. Basturk. 2018. “Performance Improvement of Deep Neural Network Classifiers by a Simple Training Strategy.” Engineering Applications of Artificial Intelligence 67 (January): 14–23. doi:https://doi.org/10.1016/j.engappai.2017.09.002.
   Google Scholar
• Chen, J., L. Wu, K. Audhkhasi, B. Kingsbury, and B. Ramabhadrari. 2016. “Efficient One-vs-One Kernel Ridge Regression for Speech Recognition.” In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2454–2458. doi:https://doi.org/10.1109/ICASSP.2016.7472118.
   Google Scholar
• Dai, D., and W. Yang. 2011. “Satellite Image Classification via Two-Layer Sparse Coding with Biased Image Representation.” IEEE Geoscience and Remote Sensing Letters 8 (1): 173–176. doi:https://doi.org/10.1109/LGRS.2010.2055033.
   Web of Science ®Google Scholar
• Di, Z., Q. Kang, D. Peng, and M. Zhou. 2019. “Density Peak-Based Pre-Clustering Support Vector Machine for Multi-Class Imbalanced Classification.” In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 27–32. doi:https://doi.org/10.1109/SMC.2019.8914451.
   Google Scholar
• Fonte, C. C., Minghini, M., Patriarca, J., Antoniou, V., See, L., & Skopeliti, A. (2017). Generating up-to-date and detailed land use and land cover maps using OpenStreetMap and GlobeLand30. ISPRS International Journal of Geo-Information, 6(4), 125. doi:https://doi.org/10.3390/ijgi6040125
   Web of Science ®Google Scholar
• Fu, G., C. Liu, R. Zhou, T. Sun, and Q. Zhang. 2017. “Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network.” Remote Sensing 9 (5): 498. doi:https://doi.org/10.3390/rs9050498.
   Web of Science ®Google Scholar
• Ghaffarian, S., N. Kerle, E. Pasolli, and J. J. Arsanjani. 2019. “Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data.” Remote Sensing 11 (20): 2427. doi:https://doi.org/10.3390/rs11202427.
   Web of Science ®Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative Adversarial Nets.” In Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger (Eds.)., Advances in Neural Information Processing Systems, 2672–2680. https://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014
   Google Scholar
• Grippa, T., S. Georganos, S. Zarougui, P. Bognounou, E. Diboulo, Y. Forget, M. Lennert, S. Vanhuysse, N. Mboga, and E. Wolff. 2018. “Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics.” ISPRS International Journal of Geo-Information 7 (7): 246. doi:https://doi.org/10.3390/ijgi7070246.
   Web of Science ®Google Scholar
• Haklay, M., and P. Weber. 2008. “Openstreetmap: User-Generated Street Maps.” IEEE Pervasive Computing 7 (4): 12–18. doi:https://doi.org/10.1109/MPRV.2008.80.
   Web of Science ®Google Scholar
• Heydari, S. S., and G. Mountrakis. 2018. “Effect of Classifier Selection, Reference Sample Size, Reference Class Distribution and Scene Heterogeneity in per-Pixel Classification Accuracy Using 26 Landsat Sites.” Remote Sensing of Environment 204 (January): 648–658. doi:https://doi.org/10.1016/j.rse.2017.09.035.
   Google Scholar
• Huang, B., B. Zhao, and Y. Song. 2018. “Urban Land-Use Mapping Using a Deep Convolutional Neural Network with High Spatial Resolution Multispectral Remote Sensing Imagery.” Remote Sensing of Environment 214 (September): 73–86. doi:https://doi.org/10.1016/j.rse.2018.04.050.
   Web of Science ®Google Scholar
• Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” ArXiv:1502.03167 [Cs], February. http://arxiv.org/abs/1502.03167
   Google Scholar
• I.owa Department of Natural Resources. 2009. “LandCover/LC_2009_1m (MapServer).” Iowa Department of Natural Resources. https://programs.iowadnr.gov/geospatial/rest/services/LandCover/LC_2009_1m/MapServer
   Google Scholar
• Jin, H., S. V. Stehman, and G. Mountrakis. 2014. “Assessing the Impact of Training Sample Selection on Accuracy of an Urban Classification: A Case Study in Denver, Colorado.” International Journal of Remote Sensing 35 (6): 2067–2081. doi:https://doi.org/10.1080/01431161.2014.885152.
   Web of Science ®Google Scholar
• 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:https://doi.org/10.3390/rs8040329.
   Web of Science ®Google Scholar
• Li, N., H. Hao, Z. Jiang, F. Jiang, R. Guo, G. Qing, and X. Hu. 2020. “A Multi-Task Multi-Class Learning Method for Automatic Identification of Heavy Minerals from River Sand.” Computers & Geosciences 135 (February): 104403. doi:https://doi.org/10.1016/j.cageo.2019.104403.
   Google Scholar
• Lin, T.-Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. Lawrence Zitnick. 2014. “Microsoft COCO: Common Objects in Context.” In Computer Vision – ECCV 2014, 740–755. Lecture Notes in Computer Science. Cham: Springer. doi:https://doi.org/10.1007/978-3-319-10602-1_48.
   Google Scholar
• Liu, W., Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi. 2017. “A Survey of Deep Neural Network Architectures and Their Applications.” Neurocomputing 234 (April): 11–26. doi:https://doi.org/10.1016/j.neucom.2016.12.038.
   Google Scholar
• Long, J., E. Shelhamer, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” 3431–3440. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Long_Fully_Convolutional_Networks_2015_CVPR_paper.html
   Google Scholar
• Luo, N., T. Wan, H. Hao, and Q. Lu. 2019. “Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification over Urban Areas.” Remote Sensing 11 (1): 88. doi:https://doi.org/10.3390/rs11010088.
   Web of Science ®Google Scholar
• Ma, L., M. Li, X. Ma, L. Cheng, P. Du, and Y. Liu. 2017. “A Review of Supervised Object-Based Land-Cover Image Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 130 (August): 277–293. doi:https://doi.org/10.1016/j.isprsjprs.2017.06.001.
   Web of Science ®Google Scholar
• Ma, L., Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson. 2019. “Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152 (June): 166–177. doi:https://doi.org/10.1016/j.isprsjprs.2019.04.015.
   Web of Science ®Google Scholar
• McCann, M. T., K. H. Jin, and M. Unser. 2017. “A Review of Convolutional Neural Networks for Inverse Problems in Imaging.” IEEE Signal Processing Magazine 34 (6): 85–95. doi:https://doi.org/10.1109/MSP.2017.2739299.
   Web of Science ®Google Scholar
• Panboonyuen, T., K. Jitkajornwanich, S. Lawawirojwong, P. Srestasathiern, and P. Vateekul. 2017. “Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields.” Remote Sensing 9 (7): 680. doi:https://doi.org/10.3390/rs9070680.
   Web of Science ®Google Scholar
• Rehman, S. U., T. Shanshan, O. U. Rehman, H. Yongfeng, C. M. Sarathchandra Magurawalage, and C.-C. Chang. 2018. “Optimization of CNN through Novel Training Strategy for Visual Classification Problems.” Entropy 20 (4): 290. doi:https://doi.org/10.3390/e20040290.
   Web of Science ®Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241. Munich, Germany: Springer.
   Google Scholar
• Rottensteiner, F., G. Sohn, J. Jung, M. Gerke, C. Baillard, S. Benitez, and U. Breitkopf. 2012. “The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction.” The ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1 (3): 293–298. doi:https://doi.org/10.5194/isprsannals-I-3-293-2012.
   Google Scholar
• Russakovsky, O., J. Deng, S. Hao, J. Krause, S. Satheesh, S. Ma, Z. Huang, et al. 2015. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115 (3): 211–252. doi:https://doi.org/10.1007/s11263-015-0816-y.
   Web of Science ®Google Scholar
• Scott, G. J., R. A. Marcum, C. H. Davis, and T. W. Nivin. 2017. “Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery.” IEEE Geoscience and Remote Sensing Letters 14 (9): 1638–1642. doi:https://doi.org/10.1109/LGRS.2017.2722988.
   Web of Science ®Google Scholar
• Sherrah, J. 2016. “Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery.” ArXiv:1606.02585 [Cs], June. http://arxiv.org/abs/1606.02585
   Google Scholar
• Xinhua. 2017. “China Unveils Results of First Geoinformation Survey.” People’s Daily Online. People’s Daily Online. April 25. http://en.people.cn/n3/2017/0425/c90000-9207010.html
   Google Scholar
• Yang, Y., and S. Newsam. 2010. “Bag-of-Visual-Words and Spatial Extensions for Land-Use Classification.” In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 270–279. GIS ’10. New York, NY: ACM. doi:https://doi.org/10.1145/1869790.1869829.
   Google Scholar
• Zeiler, M. D., and R. Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In Computer Vision – ECCV 2014, edited by D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, 818–33.Lecture Notes in Computer Science. Zurich, Switzerland: Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-10590-1
   Google Scholar
• Zhang, C., I. Sargent, X. Pan, H. Li, A. Gardiner, J. Hare, and P. M. Atkinson. 2018. “An Object-Based Convolutional Neural Network (OCNN) for Urban Land Use Classification.” Remote Sensing of Environment 216: 57–70. doi:https://doi.org/10.1016/j.rse.2018.06.034.
   Web of Science ®Google Scholar
• Zhao, W., Y. Bo, J. Chen, D. Tiede, T. Blaschke, and W. J. Emery. 2019. “Exploring Semantic Elements for Urban Scene Recognition: Deep Integration of High-Resolution Imagery and OpenStreetMap (OSM).” ISPRS Journal of Photogrammetry and Remote Sensing 151 (May): 237–250. doi:https://doi.org/10.1016/j.isprsjprs.2019.03.019.
   Google Scholar
• Zhen, Z., L. J. Quackenbush, S. V. Stehman, and L. Zhang. 2013. “Impact of Training and Validation Sample Selection on Classification Accuracy and Accuracy Assessment When Using Reference Polygons in Object-Based Classification.” International Journal of Remote Sensing 34 (19): 6914–6930. doi:https://doi.org/10.1080/01431161.2013.810822.
   Web of Science ®Google Scholar
• Zhu, X., C. Vondrick, C. C. Fowlkes, and D. Ramanan. 2016. “Do We Need More Training Data?” International Journal of Computer Vision 119 (1): 76–92. doi:https://doi.org/10.1007/s11263-015-0812-2.
   Web of Science ®Google Scholar
• Zhu, X. X., D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, and F. Fraundorfer. 2017. “Deep Learning in Remote Sensing: A Review.” IEEE Geoscience and Remote Sensing Magazine 5 (4): 8–36. doi:https://doi.org/10.1109/MGRS.2017.2762307.
   Web of Science ®Google Scholar