Abstract
Cloud detection is a crucial step for automatic satellite image analysis. Some cloud detection methods exploit specially designed spectral bands, other base the detection on time series, or on the inter-band delay in push-broom satellites. Nevertheless many use cases occur where these methods do not apply. This paper describes a convolutional neural network for cloud detection in panchromatic and single-frame images. Only a per-image annotation is required, indicating which images contain clouds and which are cloud-free. Our experiments show that, in spite of using less information, the proposed method produces competitive results.
| Original language | English |
|---|---|
| Title of host publication | IGARSS 2021: 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
| Publisher | IEEE |
| Pages | 4964-4967 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781665403696 |
| ISBN (Print) | 9781665447621 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021 |
Conference
| Conference | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 |
|---|---|
| Country/Territory | Belgium |
| City | Brussels |
| Period | 12/07/21 → 16/07/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE
Funding
Work partly financed by IDEX Paris-Saclay IDI 2016, ANR-11-IDEX-0003-02, Office of Naval research grant N00014-17-1-2552, DGA Astrid project ≪ filmer la Terre ≫ noANR-17-ASTR-0013-01, MENRT. The Titan V used for this research was donated by the NVIDIA Corporation.
Keywords
- Cloud detector
- CNN
- panchromatic
- satellite images
- single-band