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.date.accessioned | 2007-07-05T14:11:26Z | en_US |
dc.date.available | 2007-07-05T14:11:26Z | en_US |
dc.date.issued | 2003-09 | en_US |
dc.identifier.citation | Targets and Backgrounds IX: Characterization and Representation 5075 (2003) 367-377 | en_US |
dc.identifier.issn | 0277-786X | en_US |
dc.identifier.uri | http://hdl.handle.net/1850/4243 | en_US |
dc.description | RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/ | |
dc.description.abstract | Fire detection has been an active research field for many years and a number of algorithms have been proposed.
These algorithms, however, are often inflexible in dealing with the spatial and temporal heterogeneity of the
environment. Different biomes, seasons, and temperatures usually cause the performance of these algorithms
to vary dramatically. In this paper, we propose a new algorithm for fire detection based on the Mahalanobis
distance that exploits the statistical properties of multi-spectral images. The distinguishing feature of our
algorithm is its robustness. It can effectively differentiate fire from background in various environments, using
a single, fixed threshold. We evaluate our algorithm by comparing it to three state-of-the-art existing algorithms:
the MODVOLC normalized fire index algorithm, the Arino’s threshold algorithm, and the contextual
MODIS algorithm. All algorithms are tested using MODIS images taken in different parts of the world as
well as at different times. Experimental results demonstrate that our algorithm consistently achieves the best
performance, showing a low and constant false alarm rate. | en_US |
dc.description.sponsorship | n/a | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | The International Society for Optical Engineering (SPIE) | en_US |
dc.relation.ispartofseries | vol. 5075 | en_US |
dc.subject | Fire detection | en_US |
dc.subject | Forest fires | en_US |
dc.subject | Mahalanobis distance | en_US |
dc.subject | MODIS | en_US |
dc.title | A new algorithm for global forest fire detection using multispectral images | en_US |
dc.type | Article | en_US |
dc.identifier.url | http://dx.doi.org/10.1117/12.516544 | |