Research on Forest Fire Smoke and Cloud Separation Method Based on Fisher Discriminant Analysis

In remote sensing monitoring of forest fires, smoke and clouds exhibit similar spectral characteristics in satellite imagery, which can easily lead to clouds being misjudged as smoke. This incorrect discrimination may result in missed detections or false alarms of fire points. The precise differentiation of smoke and clouds has become increasingly challenging, significantly limiting the ability to accurately identify fires in their early stages. Additionally, electromagnetic waves penetrating the smoke and clouds interact with the underlying surface, which interferes with the effective separation of smoke and clouds. In response to the aforementioned issues, this paper systematically studies the impact mechanism of different underlying surfaces on the spectral response of smoke and clouds. We constructed a dataset using sample collection and gradation methods. It contains smoke at varying concentrations and clouds of different thicknesses over three typical underlying surfaces: vegetation, soil, and water. Based on the analysis of spectral characteristics, analysis of variance (ANOVA) was applied to screen sensitive bands suitable for the separation of smoke and clouds. Furthermore, considering the distribution characteristics of smoke and cloud samples in spectral space, single-band threshold models, visible-band index (VBI) models, ratio index models, and Fisher smoke and cloud recognition index (FSCRI) models were developed for three typical underlying surfaces. The validation results demonstrate that the FSCRI models significantly outperform other models in terms of both robustness and accuracy. Their recognition accuracy rates for smoke and clouds in the underlying surfaces of vegetation, soil and water reached 95.5%, 93.5% and 99%, respectively. The proposed method effectively suppresses cloud interference to improve smoke and cloud separation. This capability enables more accurate early detection of forest fires and localization of their sources.