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Proceedings Paper

Detection of thermal anomalies (fires) by a nonparametric pattern recognition algorithm from measurements with the AVHRR instruments
Author(s): Konstantin T. Protasov
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Paper Abstract

A problem of early detection of newly burning fires whose sizes are small is extremely actual, especially for almost inaccessible and sparsely populated regions. An approach proposed here for the detection of fires is based on methods of a pattern recognition in informative parameter spaces using information contained in indirect measurements, which in this case will be five-channel observations with the AVHRR instrument. For a class of detection and pattern recognition problems a natural informative criterion is an average risk functional. In this case the informative parameter complex is determined by minimization of this functional. Because the conditional probability density functions being mathematical models of stochastic images are unknown, a problem arises of reconstructing distributions based on learning samples. If the learning material sample length is small, it is natural to use the nonparametric Rosenblutt-Parsen estimates to reconstruct these distributions. The unknown parameters of these distributions are determined by minimization of the risk functional, when the learning sample is substituted by the empirical risk. To implement the developed algorithm, we used the data of observations with the AVHRR instrument performed in summer (May - August 1998 - 1999) over the territory of the Tomsk region, when many fires were recorded. A comparison between the results of algorithmic implementation and the operator work have shown high performance of the algorithm of detecting thermal anomalies.

Paper Details

Date Published: 17 December 1999
PDF: 8 pages
Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); doi: 10.1117/12.373103
Show Author Affiliations
Konstantin T. Protasov, Institute of Atmospheric Optics (Russia)

Published in SPIE Proceedings Vol. 3868:
Remote Sensing for Earth Science, Ocean, and Sea Ice Applications
Giovanna Cecchi; Edwin T. Engman; Eugenio Zilioli, Editor(s)

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