Design of committee machines for classification of single-wavelength lidar signals applied to early forest fire detection
Design of committee machines for classification of single-wavelength lidar signals applied to early forest fire detection
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DOI：
10.1016/j.patrec.2004.09.012
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发表时间：
2005-04-01
影响因子：
5.1
通讯作者：
Vilar, RM
中科院分区：
文献类型：
--
作者：
Fernandes, AM;Utkin, AB;Vilar, RM
The application of committee machines composed of single-layer perceptrons for the automatic classification of lidar signals for early forest fire detection is analysed. The patterns used for classification are composed of normalised lidar curve segments, pre-processed in order to reduce noise. In contrast to the approach used in previous work, these patterns contain application-specific parameters, such as peak-to-noise ratio (PNR), average amplitude ratio (AvAR) and maximum amplitude ratio (MAR), in order to improve classification efficiency. Using this method a smoke signature detection efficiency of 93% and a false alarm percentage of 0.041% were achieved for small bonfires, using an optimised committee machine composed of four single-layer perceptrons. The same committee machine was able to detect 70% of the smoke signatures in lidar return signals from large-scale fires in an early stage of development. The possibility of using a second committee machine for detecting fully developed large-scale fires is discussed. (c) 2004 Elsevier B.V. All rights reserved.
