A hybrid contextual approach to wildland fire detection using multispectral imagery

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dc.contributor.author Li, Ying en_US
dc.contributor.author Vodacek, Anthony en_US
dc.contributor.author Kremens, Robert en_US
dc.contributor.author Ononye, Ambrose en_US
dc.contributor.author Tang, Chunqiang en_US
dc.date.accessioned 2007-07-05T15:46:20Z en_US
dc.date.available 2007-07-05T15:46:20Z en_US
dc.date.issued 2005-09 en_US
dc.identifier.citation IEEE Transactions on Geoscience and Remote Sensing 43N9 (2005) 2115-2126 en_US
dc.identifier.issn 0196-2892 en_US
dc.identifier.uri http://hdl.handle.net/1850/4359 en_US
dc.description ©2005 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. en_US
dc.description.abstract 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. en_US
dc.description.sponsorship n/a en_US
dc.language.iso en_US en_US
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en_US
dc.relation.ispartofseries vol. 43 en_US
dc.relation.ispartofseries no. 9 en_US
dc.subject Anomaly detection en_US
dc.subject Mahalanobis distance en_US
dc.subject Multispectral images en_US
dc.subject Wildland fire detection en_US
dc.title A hybrid contextual approach to wildland fire detection using multispectral imagery en_US
dc.type Article en_US

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