Open Access
E3S Web Conf.
Volume 143, 2020
2nd International Symposium on Architecture Research Frontiers and Ecological Environment (ARFEE 2019)
Article Number 02015
Number of page(s) 4
Section Environmental Science and Energy Engineering
Published online 24 January 2020
  1. Rigor I G, Wallace J M, Colony R L. Response of Sea Ice to the Arctic Oscillation. Journal of Climate. 15(18):2648-2663(2002) [Google Scholar]
  2. Melling, Humfrey. Sound Scattering from Sea Ice: Aspects Relevant to Ice-Draft Profiling by Sonar. Journal of Atmospheric and Oceanic Technology. 15(4):1023-1034(1998) [Google Scholar]
  3. Zhao W, Du S. Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 54(8):4544-4554(2016) [CrossRef] [Google Scholar]
  4. DabboorM, Geldsetzer T. Towards sea ice classification using simulated RADARSAT Constellation Mission compact polarimetric SAR imagery. Remote Sensing of Environment.140:189-195(2014) [Google Scholar]
  5. Ressel R, Frost A, Lehner S. A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(7):1-9. (2015) [Google Scholar]
  6. Ressel R, Singha S, Lehner S, et al. Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(7):3131-3143(2016) [Google Scholar]
  7. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324(1998) [Google Scholar]
  8. Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems, Curran Associates Inc (2012) [Google Scholar]
  9. Chen Y S, Jiang H L, Li C Y, Jia X P and Ghamisi P. 2016. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10):6232-6251(2016) [CrossRef] [Google Scholar]
  10. Santara A, Mani K, Hatwar P, Singh A, Garg A, Padia K and Mitra P. 2017. BASS net: bandadaptive spectral-spatial feature learning neural metwork for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(9):5293-5301(2017) [CrossRef] [Google Scholar]
  11. Gong J Y, Ji S P. Photogrammetry and Deep Learning. Acta Geodaetica et Cartographica Sinica, 47(6): 693-704 (2018) [Google Scholar]
  12. Goodfellow I, Bengio Y and Courville A. Deep Learning. Cambridge: Massachusetts Institute of Technology Press, 322-334(2016) [Google Scholar]
  13. Raziye Hale Topaloğlu, Sertel E, Nebiye Musaoğlu. Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. ISPR International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8:1055-1059(2016) [CrossRef] [Google Scholar]
  14. Shen X, Zhang J, Zhang X, et al. Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly. IEEE Geoscience and Remote Sensing Letters. 14(11):1948-1952(2017) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.