IPOL Journal · Image Processing On Line
- published
- 2020-11-21
- reference
- Tristan Dagobert, Rafael Grompone von Gioi, Carlo de Franchis, Jean-Michel Morel, and Charles Hessel, Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers, Image Processing On Line, 10 (2020), pp. 167–190. https://doi.org/10.5201/ipol.2020.271
Communicated by Charles Hessel
Demo edited by Tristan Dagobert
Abstract
This paper proposes a cloud detection algorithm for Earth observation images obtained by pushbroom satellite imagers. The pushbroom technology induces an inter-band acquisition delay leading to a parallax effect for the clouds. We propose a method exploiting this characteristic thanks to the analysis of the inter-band disparity. Several other features discriminating clouds are also defined and all are merged to build a robust a contrario statistical decision. Experiments applied on scenes acquired by various pushbroom satellites such as Sentinel-2, RapidEye and WorldView-2 show the effectiveness of the proposed method. In particular, we demonstrate a balanced accuracy rate close to 98% for cloud and non cloud classification for Sentinel-2 images.
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- source code: ZIP
Non-Reviewed Supplementary Materials
These files and information are provided by the authors and have not been reviewed.
- This dataset is a 'light' version of the original dataset presented by Hollstein et al. in the paper: A. Hollstein, K. Segl, L. Guanter, M. Brell and M. Enesco, “Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images”, Remote Sensing, vol. 8, no. 8, p. 666, Aug. 2016. [Online]. Available:http://www.mdpi.com/2072-4292/8/8/666
It consists of 9854 RGB patches of size 366x366, extracted from the original tiles of size 10980x10980 with their associated ground truth.
History
- Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.
