A hybrid contextual approach to wildland fire detection using multispectral imagery

• Published in: IEEE transactions on geoscience and remote sensing Vol. 43; no. 9; pp. 2115 - 2126
• Main Authors: Ying Li, Vodacek, A., Kremens, R.L., Ononye, A., Chunqiang Tang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-09-2005; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Anomaly detection; Applied geophysics; Bands; Detection algorithms; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fire detection; Fires; High-resolution imaging; Infrared detectors; Internal geophysics; Mahalanobis distance; MODIS; multispectral images; Multispectral imaging; Pixel; Pixels; Predictive models; Satellites; Studies; Thresholds; wildland fire detection; algorithms; detection; probability; multispectral remote sensing; Space remote sensing; accuracy; covariance; anomalies; Anomaly detection; fires; propagation; imagery; wildland fire detection; multispectral images; Pixel; Mahalanobis distance
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2005.853935

We propose a hybrid contextual fire detection algorithm for airborne and satellite thermal images. The proposed algorithm essentially treats fire pixels as anomalies in images and can be considered a special case of the more general clutter or background suppression problem. It utilizes the local background around a potential fire pixel and discriminates fire pixels based on the squared Mahalanobis distance in multispectral feature space. It also employs the normalized thermal index to identify background fire pixels that should be excluded from the calculation of the statistical properties of the local background. The use of the squared Mahalanobis distance naturally incorporates the covariance of the multispectral image into the decision and requires the setting of a single detection threshold. By contrast, previous contextual algorithms only incorporate the statistical properties of individual bands and require the manual setting of multiple thresholds. Compared with the latest Moderate Resolution Imaging Spectroradiometer fire product (version 4), our algorithm improves user accuracy and producer accuracy by 1.5% and 2.6% on average, respectively, and up to 28% for some images. In addition, the novel use of the squared Mahalanobis distance allows us to create fire probability images that are useful for fire propagation modeling. As an example, we demonstrate this use for the airborne data.