Paper
17 December 1999 Detection of thermal anomalies (fires) by a nonparametric pattern recognition algorithm from measurements with the AVHRR instruments
Konstantin T. Protasov
Author Affiliations +
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Konstantin T. Protasov "Detection of thermal anomalies (fires) by a nonparametric pattern recognition algorithm from measurements with the AVHRR instruments", Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); https://doi.org/10.1117/12.373103
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KEYWORDS
Statistical analysis

Pattern recognition

Detection and tracking algorithms

Algorithm development

Satellites

Stochastic processes

Aerospace engineering

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