Have a personal or library account? Click to login
Land Cover Classification using Very High Spatial Resolution Remote Sensing Data and Deep Learning Cover

Land Cover Classification using Very High Spatial Resolution Remote Sensing Data and Deep Learning

By: R. Ķēniņš  
Open Access
|May 2020

References

  1. 1. Chatterjee, S. (2017). Different Kinds of Convolutional Filters. Available at https://www.saama.com/blog/different-kinds-convolutional-filters/
  2. 2. Prabhu, R. (2018). Understanding of Convolutional Neural Network (CNN) – Deep Learning. Available at https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148
  3. 3. Carter, J., Schmid, K., Waters, K., Betzhold, L., Hadley, B., Mataosky, R., & Halleran, J. (2012). Lidar 101: An Introduction to Lidar Technology, Data, and Applications. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, Charleston, South Carolina. Available at https://coast.noaa.gov/data/digitalcoast/pdf/lidar-101.pdf
  4. 4. Yodin. (2015). Surfaces Represented by a Digital Surface Model and Digital Terrain Model. Licensed under the Creative Commons Attribution-Share Alike 4.0 International license (https://creativecommons.org/licenses/by-sa/4.0/deed.en). Available at https://commons.wikimedia.org/wiki/File:DTM_DSM.svg
  5. 5. Lamba, H. (n.d.). Understanding Semantic Segmentation with UNET. Available at https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47
  6. 6. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Available at https://arxiv.org/abs/1505.0459710.1007/978-3-319-24574-4_28
DOI: https://doi.org/10.2478/lpts-2020-0009 | Journal eISSN: 2255-8896 | Journal ISSN: 0868-8257
Language: English
Page range: 71 - 77
Published on: May 11, 2020
Published by: Institute of Physical Energetics
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2020 R. Ķēniņš, published by Institute of Physical Energetics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.