@article{ref1,
title="Forest fire progress monitoring using dual-polarisation Synthetic Aperture Radar (SAR) images combined with multi-scale segmentation and unsupervised classification",
journal="International journal of wildland fire",
year="2024",
author="Shama, Age and Zhang, Rui and Wang, Ting and Liu, Anmengyun and Bao, Xin and Lv, Jichao and Zhang, Yuchun and Liu, Guoxiang",
volume="33",
number="1",
pages="WF23124-WF23124",
abstract="Background The cloud-penetrating and fog-penetrating capability of Synthetic Aperture Radar (SAR) give it the potential for application in forest fire progress monitoring; however, the low extraction accuracy and significant salt-and-pepper noise in SAR remote sensing mapping of the burned area are problems.Aims This paper provides a method for accurately extracting the burned area based on fully exploiting the changes in multiple different dimensional feature parameters of dual-polarised SAR images before and after a fire.<br><br>METHODS This paper describes forest fire progress monitoring using dual-polarisation SAR images combined with multi-scale segmentation and unsupervised classification. We first constructed polarisation feature and texture feature datasets using multi-scene Sentinel-1 images. A multi-scale segmentation algorithm was then used to generate objects to suppress the salt-and-pepper noise, followed by an unsupervised classification method to extract the burned area.Key results The accuracy of burned area extraction in this paper is 91.67%, an improvement of 33.70% compared to the pixel-based classification results.<br><br>CONCLUSIONS Compared with the pixel-based method, our method effectively suppresses the salt-and-pepper noise and improves the SAR burned area extraction accuracy.Implications The fire monitoring method using SAR images provides a reference for extracting the burned area under continuous cloud or smoke cover.<p /> <p>Language: en</p>",
language="en",
issn="1049-8001",
doi="10.1071/WF23124",
url="http://dx.doi.org/10.1071/WF23124"
}