Segmentation of polarimetric radar imagery using statistical texture

Guyot, Adrien; Brook, Jordan P.; Protat, Alain; Turner, Kathryn; Soderholm, Joshua; McCarthy, Nicholas F.; McGowan, Hamish

doi:https://doi.org/10.5194/amt-16-4571-2023

Articles | Volume 16, issue 19

https://doi.org/10.5194/amt-16-4571-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-16-4571-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 19

Research article

|

12 Oct 2023

Research article |

| 12 Oct 2023

Segmentation of polarimetric radar imagery using statistical texture

Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan

Data sets

Australian Operational Weather Radar Level 1 Dataset J. Soderholm, A. Protat, and C. Jakob https://doi.org/10.25914/508X-9A12

Australian Operational Weather Radar Level 1b Dataset J. Soderholm, V. Louf, J. Brook, and A. Protat https://doi.org/10.25914/40KE-NM05

NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT distributed from NASA FIRMS NASA Earth Data https://doi.org/10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002

MODIS Collection 61 NRT Hotspot/Active Fire Detections MCD14DL distributed from NASA FIRMS NASA Earth Data https://doi.org/10.5067/FIRMS/MODIS/MCD14DL.NRT.0061

Short summary

We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.