A Burned Area Extracting Method Using Polarization and Texture Feature of Sentinel-1A Images

• Published in: IEEE geoscience and remote sensing letters Vol. 20; p. 1
• Main Authors: Shama, Age, Zhang, Rui, Zhan, Runqing, Wang, Ting, Xie, Lingxiao, Bao, Xin, Lv, Jichao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backscatter; Backscattering; backscattering strength; burned area; Data acquisition; Environmental factors; Feature extraction; Fog; Forest ecosystems; Forest fires; Forestry; Image segmentation; Machine learning; Methods; Optical properties; Polarization; polarization decomposition; Radar polarimetry; random forest; Random forests; Scattering; Synthetic aperture radar; Terrestrial ecosystems; Texture; texture feature
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3246117

Forest fire not only seriously affects the stability of the forest ecosystem but also threatens the safety of human life and property. The previous burned areas extracting method mainly focuses on optical images, susceptible to cloud and fog objective environmental factors. Although there are related studies on the threshold segmentation of single SAR feature types, further information mining for SAR image data, e.g., backscattering intensity, polarization decomposition, texture, and other features, are still insufficient. Therefore, this paper proposes a burned area extracting method using polarization and texture features of Sentinel-1A images to combine and comprehensively mine various SAR feature change information caused by forest fires with a random forest model. For validation purposes, we compared the burned areas' extracted results with the reference data acquired based on Sentinel-2A optical imagery. The comparative results show that the SAR extraction results highly agree with the reference data, with an accuracy of 87.12%, and the commission and omission errors were 20.44% and 12.88%, respectively. The proposed machine learning method helps extract fire areas covered by thick smoke or persistent clouds and provides a reference to related research.