A hybrid of deep learning and hand-crafted features based approach for snow cover mapping

References

• Basu, S., M. Karki, S. Mukhopadhyay, S. Ganguly, R. Nemani, R. DiBiano, and S. Gayaka, (2016, July). “A Theoretical Analysis of Deep Neural Networks for Texture Classification.” In Neural Networks (IJCNN), 2016 International Joint Conference (pp. 992–999). IEEE. doi:10.3389/fpsyg.2016.00992
   Google Scholar
• Bates, B. C., Z. W. Kundzewicz, S. Wu, and J. P. Palutikof. 2008. Climate Change and Water. Geneva, Switzerland: Intergovernmental panel on climate change, IPCC secretariat.
   Google Scholar
• Baumgartner, M. F., and G. Apfl. 1994. Monitoring a Snow Cover Variations in the Alps Using the Alpine Snow Cover Analysis System (ASCAS). Mountain Environments in Changing Climates, 108–120. New York, NY, USA: Routledge.
   Google Scholar
• Beck, J. R., and E. K. Shultz. 1986. “The Use of Relative Operating Characteristic (ROC) Curves in Test Performance Evaluation.” Archives of Pathology Laboratory Medicine 110 (1): 13–20.
   PubMed Web of Science ®Google Scholar
• Czyzowska-Wisniewski, E. H., W. J. Van Leeuwen, K. K. Hirschboeck, S. E. Marsh, and W. T. Wisniewski. 2015. “Fractional Snow Cover Estimation in Complex Alpine-Forested Environments Using an Artificial Neural Network.” Remote Sensing of Environment 156: 403–417. doi:10.1016/j.rse.2014.09.026.
   Web of Science ®Google Scholar
• Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012. “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026.
   Web of Science ®Google Scholar
• Frei, A., M. Tedesco, S. Lee, J. Foster, D. K. Hall, R. Kelly, and D. A. Robinson. 2012. “A Review of Global Satellite-Derived Snow Products.” Advances in Space Research 50 (8): 1007–1029. doi:10.1016/j.asr.2011.12.021.
   Web of Science ®Google Scholar
• Geng, J., J. Fan, H. Wang, X. Ma, B. Li, and F. Chen. 2015. “High-Resolution SAR Image Classification via Deep Convolutional Autoencoders.” IEEE Geoscience and Remote Sensing Letters 12 (11): 2351–2355. doi:10.1109/LGRS.2015.2478256.
   Web of Science ®Google Scholar
• Gunn, S. R. 1998. “Support Vector Machines for Classification and Regression.” ISIS Technical Report 14 (1): 5–16.
   Google Scholar
• Hall, D. K., G. A. Riggs, and V. V. Salomonson. 1995. “Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data.” Remote Sensing of Environment 54 (2): 127–140. doi:10.1016/0034-4257(95)00137-P.
   Web of Science ®Google Scholar
• Hughes, M. G., and D. A. Robinson. 1996. “Historical Snow Cover Variability in the Great Plains Region of the USA: 1910 through to 1993.” International Journal of Climatology 16 (9): 1005–1018. doi:10.1002/(ISSN)1097-0088.
   Web of Science ®Google Scholar
• Imandoust, S. B., and M. Bolandraftar. 2013. “Application of K-Nearest Neighbor (Knn) Approach for Predicting Economic Events: Theoretical Background.” International Journal of Engineering Research and Applications 3 (5): 605–610.
   Google Scholar
• Ishii, T., R. Nakamura, H. Nakada, Y. Mochizuki, and H. Ishikawa, 2015, “May. Surface Object Recognition with CNN and SVM in Landsat 8 Images.” In Machine Vision Applications (MVA), 2015 14th IAPR International Conference (pp. 341–344). IEEE. doi:10.4103/1319-3767.166209
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, 1097–1105. Red Hook, NY: Curran Associates, Inc.
   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 (5): 778–782. doi:10.1109/LGRS.2017.2681128.
   Web of Science ®Google Scholar
• Kussul, N., G. Lemoine, F. J. Gallego, S. V. Skakun, M. Lavreniuk, and A. Y. Shelestov. 2016a. “Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and sentinel-1A Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (6): 2500–2508. doi:10.1109/JSTARS.2016.2560141.
   Web of Science ®Google Scholar
• Kussul, N. N., N. S. Lavreniuk, A. Y. Shelestov, B. Y. Yailymov, and I. N. Butko. 2016b. “Land Cover Changes Analysis Based on Deep Machine Learning Technique.” Journal of Automation and Information Sciences 48: 5.
   Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Lee, H., P. Pham, Y. Largman, and A. Y. Ng. 2009. “Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks.” In Advances in Neural Information Processing Systems, edited by Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams and A. Culotta, 1096–1104. Red Hook, NY: Curran Associates, Inc.
   Google Scholar
• Li, J., B. Xi, Y. Li, Q. Du, and K. Wang. 2018. “Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks.” Remote Sensing 10 (3): 396. doi:10.3390/rs10030396.
   Web of Science ®Google Scholar
• Liao, Y., and V. R. Vemuri. 2002. “Use of K-Nearest Neighbor Classifier for Intrusion Detection.” Computers Security 21 (5): 439–448. doi:10.1016/S0167-4048(02)00514-X.
   Web of Science ®Google Scholar
• Madzarov, G., D. Gjorgjevikj, and I. Chorbev. 2009. “A Multi-Class SVM Classifier Utilizing Binary Decision Tree.” Informatica 33: 2.
   Google Scholar
• Majtner, T., S. Yildirim-Yayilgan, and J. Y. Hardeberg, (2016, December). “Combining Deep Learning and Hand-Crafted Features for Skin Lesion Classification.” In Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference (pp. 1–6). IEEE.
   Google Scholar
• Metsämäki, S., O. P. Mattila, J. Pulliainen, K. Niemi, K. Luojus, and K. Böttcher. 2012. “An Optical Reflectance Model-Based Method for Fractional Snow Cover Mapping Applicable to Continental Scale.” Remote Sensing of Environment 123: 508–521. doi:10.1016/j.rse.2012.04.010.
   Web of Science ®Google Scholar
• Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
   Web of Science ®Google Scholar
• Paola, J. D., and R. A. Schowengerdt. 1995. “A Review and Analysis of Backpropagation Neural Networks for Classification of Remotely-Sensed Multi-Spectral Imagery.” International Journal of Remote Sensing 16 (16): 3033–3058. doi:10.1080/01431169508954607.
   Web of Science ®Google Scholar
• Paul, F., C. Huggel, and A. Kääb. 2004. “Combining Satellite Multispectral Image Data and a Digital Elevation Model for Mapping Debris-Covered Glaciers.” Remote Sensing of Environment 89 (4): 510–518. doi:10.1016/j.rse.2003.11.007.
   Web of Science ®Google Scholar
• Penatti, O. A., K. Nogueira, and J. A. Dos Santos, 2015. Do Deep Features Generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, June 7–12, 44–51.
   Google Scholar
• Platt, J. 1999. “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods.” Advances in Large Margin Classifiers 10 (3): 61–74.
   Google Scholar
• Prestrud, P. 2007. Global Outlook for Ice Snow. Nairobi: UNEP/Earthprint.
   Google Scholar
• Shi, J., J. Dozier, and H. Rott. 1994. “Snow Mapping in Alpine Regions with Synthetic Aperture Radar.” IEEE Transactions on Geoscience and Remote Sensing 32 (1): 152–158. doi:10.1109/36.285197.
   Web of Science ®Google Scholar
• Shukla, A., and B. Yousuf. 2017. “Evaluation of Multisource Data for Glacier Terrain Mapping: A Neural Net Approach.” Geocarto International 32 (5): 569–587.
   Web of Science ®Google Scholar
• Simpson, J. J., and J. R. Stitt. 1998. “A Procedure for the Detection and Removal of Cloud Shadow from AVHRR Data over Land.” IEEE Transactions on Geoscience and Remote Sensing 36 (3): 880–897. doi:10.1109/36.673680.
   Web of Science ®Google Scholar
• Simpson, J. J., J. R. Stitt, and M. Sienko. 1998. “Improved Estimates of the Areal Extent of Snow Cover from AVHRR Data.” Journal of Hydrology 204 (1–4): 1–23.
   Web of Science ®Google Scholar
• Sun, Y., A. K. Wong, and M. S. Kamel. 2009. “Classification of Imbalanced Data: A Review.” International Journal of Pattern Recognition and Artificial Intelligence 23 (04): 687–719.
   Web of Science ®Google Scholar
• Vedaldi, A., and K. Lenc, 2015, October. “Matconvnet: Convolutional Neural Networks for Matlab.” In Proceedings of the 23rd ACM international conference on Multimedia (pp. 689–692). ACM.
   Google Scholar
• Way, T. W., B. Sahiner, L. M. Hadjiiski, and H. P. Chan. 2010. “Effect of Finite Sample Size on Feature Selection and Classification: A Simulation Study.” Medical Physics 37 (2): 907–920. doi:10.1118/1.3284974.
   PubMed Web of Science ®Google Scholar
• Wiesmeier, M., F. Barthold, B. Blank, and I. Kögel-Knabner. 2011. “Digital Mapping of Soil Organic Matter Stocks Using Random Forest Modeling in a Semi-Arid Steppe Ecosystem.” Plant and Soil 340 (1–2): 7–24. doi:10.1007/s11104-010-0425-z.
   Web of Science ®Google Scholar
• Wiltshire, A. J., G. Kay, J. L. Gornall, and R. A. Betts. 2013. “The Impact of Climate, CO2 and Population on Regional Food and Water Resources in the 2050s.” Sustainability 5 (5): 2129–2151. doi:10.3390/su5052129.
   Web of Science ®Google Scholar
• Wold, S., K. Esbensen, and P. Geladi. 1987. “Principal Component Analysis.” Chemometrics and Intelligent Laboratory Systems 2 (1–3): 37–52. doi:10.1016/0169-7439(87)80084-9.
   Web of Science ®Google Scholar
• Yan, Y. 2016. “Hierarchical Classification with Convolutional Neural Networks for Biomedical Literature.” International Journal of Computer Science and Software Engineering 5 (4): 58.
   Google Scholar
• Zhou, Y., and N. M. Cheung. 2016. Vehicle Classification Using Transferable Deep Neural Network Features. Preprint. arXiv. 1601.
   Google Scholar