Cloud detection algorithm comparison and validation for operational Landsat data products
By Steve Foga, P. Scaramuzza, Song Guo, Zhe Zhu, R. Dilley, Tim Beckmann, G. Schmidt, John L. Dwyer, M. Joseph Hughes, Brady Laue. 2017
1,077 citations recorded by the source corpus.
Subfields: Ecology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/0da009b0c81d2798a8acbc176ac63807a52ea661
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Joanne C. White
- M. Wulder
- David P. Roy
- Txomin Hermosilla
- Nicholas C. Coops
- Thomas Skjødeberg Toftegaard
- Jacob Hoxbroe Jeppesen
- Leo Lymburner
In Global and Planetary Change
- Joanne C. White
- M. Wulder
- Txomin Hermosilla
- Nicholas C. Coops
- John L. Dwyer
- Hankui K. Zhang
- David P. Roy
Citations in the corpus
- A cloud detection algorithm for satellite imagery based on deep learning (2019, methodology): “The most widely used methods for cloud detection are compared in (Foga et al., 2017), where several methods are evaluated on manually annotated Landsat 7 and 8 datasets.”
- Land cover 2.0 (2018, methodology): “…as part of the land cover classi fi cation process (Hansen et al. 2008; Lindquist et al. 2008) or removed prior to classi fi cation using cloud masking algorithms that are labelled directly in the input data (Frey et al. 2008; Foga et al. 2017; Zhu and Woodcock 2014a; Zhu, Wang, and Woodcock 2015).”
- Analysis Ready Data: Enabling Analysis of the Landsat Archive (2018, methodology): “These information are based on the CFmask cloud algorithm described in [40,41].”