The Automatic Detection of Fire Scar in Alaska using Multi-Temporal PALSAR Polarimetric SAR Data

Abstract

The existing fire scar detection methods based on multi-temporal analysis only used the difference in either backscattering intensity or polarimetric characteristics between the pre- and post-fire PolSAR data to calculate the difference image (DI), and then applied an object-based image analysis approach (OBIA) to generate the fire scar binary map. These methods all ignored the polarimetric correlation between both the pre- and post-fire PolSAR data. And many parameters of the OBIA method were determined empirically. Therefore, this study proposes a new detection method, which integrates the Hotelling–Lawley trace (HLT) statistic with a hierarchical automatic image segmentation method (GGKI-MRF). The HLT can simultaneously capture changes in both polarimetry and intensity, which helps to distinguish between burned and unburned areas. And the GGKI-MRF method is developed by combining the generalized Gaussian-based Kittler–Illingworth (GG-KI) thresholding with Markov Random Field (MRF) model, which has a good performance in reducing the isolate points (false alarms) and the omission errors. The experimental results from multi-temporal and polarimetric PALSAR PolSAR data show that the proposed method can achieve high detection accuracy (i.e., the kappa coefficient of 0.81 and the overall accuracy up to 0.92).

Keywords

• forest fire
• fire scar detection
• polarimetric sar (polsar)
• multi-temporal
• the hotelling–lawley trace (hlt)
• palsar
• automatic image segmentation
• the generalized gaussian-based kittler–illingworth (gg-ki)
• markov random field (mrf)