An Efficient Machine Learning Approach for Atmospheric Correction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s12524-021-01406-6.pdf
Reference23 articles.
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2. Agarap, Abien Fred. (2018). “Deep Learning using Rectified Linear Units (ReLU).” abs/1803.08375. http://arxiv.org/abs/1803.08375.
3. Barber, C. Bradford., Dobkin, David P., & Huhdanpaa, Hannu. (1996). The Quickhull algorithm for convex hulls. ACM Transaction on Mathematical Software, 22(4), 469–483.
4. Berk, Alexander, Anderson, Gail P, Bernstein, Lawrence S, Acharya, Prabhat K, Dothe, H., Matthew, Michael W, & Adler-Golden, Steven M, et al. (1999). “MODTRAN4 radiative transfer modeling for atmospheric correction.” In Optical spectroscopic techniques and instrumentation for atmospheric and space research III, Vol. 3756, 348–353. International Society for Optics and Photonics.
5. Felde, Gerald W, Anderson, G.P., Cooley, T.W., Matthew, M.W., Berk, A., & Lee, J. et al. (2003). “Analysis of Hyperion data with the FLAASH atmospheric correction algorithm.” In IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), Vol. 1, 90–92. IEEE.
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