Pattern Recognition Letters 26 (2005) 625–632

www.elsevier.com/locate/patrec

Design of committee machines for classificationof single-wavelength lidar signals applied to early

forest fire detection

Armando M. Fernandes a,*, Andrei B. Utkin b,Alexander V. Lavrov b,1, Rui M. Vilar a

a Departamento de Engenharia de Materiais, Instituto Superior Tecnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugalb INOV––Inesc Inovacao, Rua Alves Redol 9, 1000-029 Lisbon, Portugal

Received 17 February 2004

Abstract

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 pat-

terns 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.

� 2004 Elsevier B.V. All rights reserved.

Keywords: Lidar; Forest fire; Automatic detection; Committee machine; Single-layer perceptron

0167-8655/$ - see front matter � 2004 Elsevier B.V. All rights reserv

doi:10.1016/j.patrec.2004.09.012

* Corresponding author. Tel.: +351 21 841 81 37; fax: +351

21 841 81 20.

E-mail address: afernandes@mail.ist.utl.pt (A.M.

Fernandes).1 On leave from Russian Science Center ‘‘Applied Chemis-

try’’, St. Petersburg 197198, Russia.

1. Introduction

Extending the principles of radar to the optical

range, lidar (light detection and ranging) technol-ogy has found applications in the determination

of the position and velocity of targets, measure-

ment of the concentration of particles and

ed.

626 A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632

chemical compounds in the atmosphere and in the

study of the oceans (Measures, 1984). The success-

ful application of a single-wavelength direct lidar

technique to early forest fire detection was first

demonstrated by the authors (Utkin et al., 2002a,2003; Lavrov et al., 2003). This technique presents

considerable advantages compared to currently

used passive detection methods based on infrared

and visible cameras, such as higher sensitivity,

the possibility of accurately locating the fire, and

the ability to detect the fire even when the flames

are out of the line-of-sight from the observation

point or against a lighted background. However,the widespread application of lidar to forest fire

surveillance requires an automatic system for for-

est fire signature recognition to be made available.

The present paper describes a system based on

neural networks for the automatic detection of

smoke plumes by lidar.

The identification of smoke signatures in lidar

curves is a complex task, because the shapes ofthe peaks originating from smoke plumes are sig-

nificantly affected by random phenomena, such

as weather conditions, aerosols in the atmosphere,

etc. Neural networks have been effectively used for

the analysis of signals that are strongly influenced

by stochastic phenomena, such as radar (Haykin

and Deng, 1991), sonar (Gorman and Sejnowsky,

1988), sodar (Pal et al., 1999), and lidar (Bhatta-charya et al., 1997) signals. However, for a neural

network to be capable of automatically recognis-

ing smoke plume peaks in lidar curves strongly af-

fected by atmospheric noise, due to the wide range

of possible shapes that those peaks may present, it

should be composed of a large number of neurons,

and considerable effort would be required to train

and optimise the neural network structure. In pre-vious papers (Fernandes et al., 2002; Utkin et al.,

2002b), the authors showed that single-layer per-

ceptrons (SLP) provide satisfactory results in the

identification of smoke plume peaks in lidar

curves, despite the fact that SLPs are restricted

to solving linearly separable problems. In order

to continue using SLPs (to keep training simple)

and to be able to solve problems that are not line-arly separable (Knerr et al., 1990, 1992), commit-

tee machines (CM) (Haykin, 1999, p. 351)

composed of several SLPs were used in the present

work for the identification of smoke plume signa-

tures in lidar curves resulting from small bonfires

and large-scale fire experiments.

The SLPs that constitute the CMs are formed of

a single neuron, using a hyperbolic tangent as acti-vation function. The SLP contains one input node

with fixed input equal to +1 for bias representation

and the activation function takes values between

�1 and 1. The number of SLPs in a CM depends

on the acceptable false alarm percentage. The

SLPs from a CM are trained with the same smoke

signatures and different atmospheric noise patterns

and are expected to acquire expertise in solvingdistinct aspects of the whole classification task.

In the present situation each SLP eliminates

atmospheric noise patterns and passes on smoke

signatures to the next SLP. An alarm is generated

every time all the SLPs classify a pattern as a

smoke signature. This multistage structure helps

CMs to automatically select candidate atmos-

pheric noise patterns for training each SLP, be-cause the atmospheric noise patterns used for

this purpose are chosen randomly from among

those that previous SLPs erroneously considered

smoke signatures. Using this pattern selection pro-

cedure it is possible to ensure that all types of

atmospheric noise patterns are adequately repre-

sented by the few hundred atmospheric noise pat-

terns chosen. This allows training sets to becomposed with similar numbers of both types of

patterns, an objective that would be impossible

to achieve otherwise, because the number of

atmospheric noise patterns that can be extracted

from the experimental lidar curves is orders of

magnitude greater than the number of smoke sig-

nature patterns.

In a previous publication (Fernandes et al.,2002), the application of CMs to the classification

of lidar signals was described, but the level of false

alarms remained excessive for effective forest fire

surveillance. The present work aims to improve

pattern classification efficiency and to decrease

false alarm probability by using, beside the CMs,

a different type of training pattern that includes

different combinations of application-specificparameters such as the peak-to-noise ratio

(PNR), average amplitude ratio (AvAR) and max-

imum amplitude ratio (MAR), further to lidar

3 4 5 6 7 8 90.00

0.01

0.02

0.03

0.04

Lida

r S

igna

l (a.

u.)

Distance (km)

Fig. 1. Lidar curve with smoke signature at 6.1 km.

A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632 627

curve segments. These segments correspond to re-

gions where smoke signatures are likely to occur,

due to the presence of a local maximum. Using

them instead of the complete lidar return signal al-

lows neural networks with a smaller number of in-put nodes and weights to be used for classification.

Application-specific parameters have been em-

ployed in applications such as time series predic-

tion (Deco et al., 1997) with excellent results

because these parameters, as in the present case,

are designed to contain the underlying structure

of the data to be analysed. The different combina-

tions of application-specific parameters with thetwo types of lidar curve segments help to reduce

the tendency of SLPs to focus on the same pattern

features, a tendency that leads to increased misde-

tection probability (Fernandes et al., 2003). The

parameters PNR, AvAR and MAR measure the

difference between the central peak amplitude

and the amplitude of surrounding noise, facilitat-

ing feature identification by SLPs. The values ofthese parameters are proportional to the probabil-

ity of occurrence of a smoke signature in a pattern.

The addition of a parameter to a pattern does not

significantly increase training time, because it

means one weight is added to the SLP. Moreover,

the calculation of PNR, AvAR and MAR is extre-

mely fast, due to their simple definitions, so it does

not significantly increase the time required for clas-sifying each pattern. Finally, it is shown that CMs

trained with smoke signatures resulting from small

bonfires effectively detect smoke signatures from

large-scale forest fires.

2. Pattern description

Lidar curves containing smoke signatures were

experimentally obtained, on one hand, from bon-

fires with a burning rate of 0.02kg/s and, on the

other hand, from large-scale fires with burning

rates of about 10kg/s, as part of the Gestosa

2003 test campaign (Viegas, 2000, 2003). The tests

were carried out using a lidar based on a Nd:YAG

laser. The experimental technique used was de-scribed in (Lavrov et al., 2003; Utkin et al.,

2003). A first series of tests was carried out using

two radiation wavelengths, 532 and 1064 nm,

and a laser beam divergence of 0.5mrad. The laser

energy was varied in the range 2–20 and 6–60 mJ

for 532 and 1064 nm radiation, respectively. A sec-

ond series of experiments was performed with

1064nm laser radiation, a larger laser beam diver-

gence (2mrad) and pulse energy of 150 mJ. Thewind velocity during the experiments ranged from

nearly 0 to 50 km per hour. Lidar curves were col-

lected during daytime, in sunny or cloudy weather,

as well as during the night and at sunset. Each li-

dar curve resulted from the accumulation of be-

tween 32 and 256 laser pulse returns and is

composed of 2000 backscattered-power measure-

ments (see Fig. 1). Varying the number of pulse re-turn signals enables different signal-to-noise ratios

to be obtained, for a given set of experimental

conditions.

2.1. Types of lidar curve segments

The segmentation of lidar curves is possible be-

cause the smoke signature shape is independent ofdistance. It also simplifies calculating the exact dis-

tance to the fire in the event of an alarm. The lidar

curves were therefore divided in regions-of-interest

(ROI) consisting of 41 points, whenever a local

maximum coincided with the centre of the ROI.

The width of the ROIs was chosen so that, on

one hand, the ROIs contain enough information

to allow a low false alarm probability and, onthe other hand, the SLPs have a small number of

weights and are easy to train. The influence of

0 10 20 30 40

Point Index

PNR=P/∆

P

∆

Linear Fit

0 10 20 30 40 50 60 70 80

m 1

M1 M2

m 2

Point Index

A2

A 1

MAR=A1 /A2Regi on of In te rest

0 20 40 60 80 100 120

M4

m4m3

Point Index

AvAR=A3/A4

A3

A4

Region of Interest

M3

Lida

r S

igna

l (a.

u.)

Lida

r S

igna

l (a.

u.)

Lida

r S

igna

l (a.

u.)

Fig. 2. Definition of parameters PNR, MAR and AvAR.

628 A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632

the number of points in the patterns on the results

achieved has been analysed elsewhere (Fernandes

et al., 2003). The ROIs were normalised to mini-

mum and maximum values of �0.9 and 0.9,

respectively, in order to make them independentof the scale and of the time-dependent background

originating from electronics and atmospheric

noise. The normalisation also speeds up neural

network training by avoiding weight values that

are, in modulus, much larger or smaller than unity.

The analysis of the ROI enables identification of

smoke plumes based on the characteristic shape

of smoke plume peaks in lidar curves, but somedifficulties are expected due to the noisiness of

the lidar signal. To overcome this limitation trans-

formed lidar curve segments have been created on

the basis of the normalised ROIs previously de-

fined. Each segment consists of nine points whose

values are proportional to the percentage of points

from an ROI whose value exceeds a certain thresh-

old between �0.9 and 0.9. In other words, thetransformed lidar curve segments are the cumula-

tive frequency distribution of values of an ROI.

They enable the classification to be performed on

the basis of the distribution of segment values,

but some of the noise contained in the 41-point

segments is eliminated. Again, the fact that the

transformed segments contain a lower number of

points than the 41-point segments helps to reducethe training time, by reducing the number of

weights to be calculated.

2.2. Parameters to characterise smoke plume

signatures

As previously mentioned, the patterns used in

the numerical experiments consist of either theROI or transformed lidar curve segments, associ-

ated with the problem-specific parameters, peak-

to-noise ratio (PNR), average amplitude ratio

(AvAR) and maximum amplitude ratio (MAR)

(see Fig. 2). The peak-to-noise ratio is an extension

of the signal-to-noise ratio (SNR) concept to situ-

ations where the central peak results from atmos-

pheric noise and not from a smoke plume. It wasdefined as the ratio between the peak amplitude

(P in Fig. 2a) and the standard deviation of the

ROI point values (D in Fig. 2a), excluding seven

points that, typically, correspond to the width

of peaks resulting from small bonfire smoke

plumes. The amplitude of the central peak and

the standard deviation are calculated relative to

the background, defined by a root-mean-square

interpolation of the pattern points, excluding the

95

100

ture

s(%

)

x epochsy epochsz epochs

Neyman-PearsonFalse Alarm Level

A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632 629

seven central points (linear fit in Fig. 2a). The max-

imum amplitude ratio (Fig. 2b), is defined as the

ratio A1/A2, where A1 is the difference between

the maximum (M1) and the minimum (m1) in an

ROI, and A2 is the difference between the maxi-mum (M2) and minimum (m2) values of the 41

points located to the right of the ROI:

MAR ¼ M1 � m1

M2 � m2

¼ A1

A2

The average amplitude ratio (Fig. 2c) is the

ratio between the amplitude of the central peak

in the ROI (A3) and the backscattered-power value

in the central peak position (A4) of the line that

connects the maxima of the 41 points to the left

and to the right of the ROI (M3 and M4):

AvAR ¼ A3

A4

A3 and A4 are calculated relative to the back-

ground defined by a straight line connecting the

minima of the 41 points to the left and to the right

of the ROI (m3 and m4).The purpose of the PNR is to compare the cen-

tral peak amplitude with the average amplitude

within the ROI points. The aim of the parameter

MAR is to detect sudden increases in signal ampli-

tude, by comparing amplitude within the ROI

points with the amplitude of points to its right.

The AvAR compares the actual ROI central peak

amplitude with its expectable amplitude by calcu-lating the ratio between the central peak amplitude

and an average amplitude calculated taking into

consideration the maximum values to the left and

to the right of the ROI.

0 2 4 6 8 10 12 14 16 1880

85

90

Det

ecte

dS

mok

eS

igna

False Alarms (%)

Fig. 3. Example of the early stopping method in which it can be

seen that ‘‘y epochs’’ (y is larger than x and smaller than z)

provide the best true detection percentage for the established

Neyman–Pearson criterion.

3. Single-layer perceptron training procedure

The training of SLPs was carried out by mini-

mising the sum-of-squares cost function, with an

algorithm based on backpropagation and called

polynomial approximation with periodically re-

started conjugate gradient (PPRCG), described in

previous papers (Fernandes et al., 2002; Utkin

et al., 2002b). This algorithm uses conjugate gradi-

ent and polynomial interpolation to calculate thedescent direction and the optimal learning rate

for each training epoch, respectively. The conju-

gate gradient is calculated from the second-order

derivatives of the error function, estimated using

a recursive method that is computationally less

demanding than the explicit determination of sec-ond-order derivatives (Yu et al., 1995). In order

to estimate the classification error reliably, a 10-

fold cross-validation method was used (Haykin,

1999, p. 217). In this method the available training

patterns are divided into 10 groups. Then 10 neu-

ral networks are trained with the patterns in nine

groups and validated with the patterns in the

remaining group. The cross-validation error is de-fined as the sum of the errors of the 10 neural net-

works in the classification of the validation groups.

The outcome of the 10-fold cross-validation meth-

od, besides the cross-validation error, is a neural

network trained with all patterns in the groups.

In order to avoid overfitting (characterised by an

increase in the cross-validation error), the early

stopping method (Prechelt, 1998; Haykin, 1999,p. 215) was applied. Early stopping was executed

using the Neyman–Pearson criterion and relative

operating characteristic (ROC) curves built using

the results of different numbers of training epochs

(Fernandes et al., 2003). ROC curves (Freking

et al., 1998) are plots of the percentage of detected

smoke signatures as a function of the false alarm

630 A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632

percentage. They are constructed by varying the

neural network detection threshold, defined as

the output value above which a pattern is classified

as a smoke signature. According to the Neyman–

Pearson criterion (Haykin, 1999, p. 28), the detec-tion threshold and the largest number of training

epochs before overfitting must maximise the per-

centage of detected smoke signatures, subject to

the constraint that the percentage of false alarms

should not exceed a prescribed value (see Fig. 3).

The maximum percentage of false alarms allowed

is chosen taking into consideration that decreasing

the false alarm percentage causes a decrease in thepercentage of detected events.

4. Committee machine design and results

Since smoke signatures resulting from small

bonfires present narrow peaks, which differ consid-

erably from the complex shaped smoke signaturesresulting from large-scale fires (see Fig. 4), two

CMs were designed to detect smoke signatures

resulting from small- and large-scale fires, respec-

tively. The first CM is composed of four SLPs,

with all classifying patterns made up of 41-point li-

dar curve segments, together with the PNR for the

first two SLPs, PNR and MAR for the third and

PNR, MAR and AvAR for the fourth. The secondCM is composed of three SLPs. One SLP classifies

0 5 10 15 20 25 30 35 40-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Nor

mal

ized

Lid

ar C

urve

Seg

men

ts

Pattern Point Index

Small BonfireLarge-scale Fire

Fig. 4. Comparison of lidar curve segments of a smoke

signature from a small bonfire and a smoke signature from a

large-scale fire.

patterns formed by 41-point lidar curve segments

and MAR, while the other two classify patterns

formed by transformed lidar curve segments and

AvAR or MAR. The number of SLPs in the CM

structures was chosen so that the percentage offalse alarms obtained by cross-validation does

not exceed 0.012%. This represents a 10-fold

improvement as compared to the results previously

reported (Fernandes et al., 2002). Of the combina-

tions tested, the combinations of lidar curve seg-

ments with application-specific parameters

chosen for the SLPs were the ones that enabled

the lowest percentage of misdetections by cross-validation to be achieved.

The first CM training set consisted of 141

smoke signatures from small bonfires and 670

atmospheric noise patterns. The second CM was

trained with 69 large-scale fire smoke signatures

presenting a shape so complex that they are mis-

classified by the first CM, and 400 atmospheric

noise patterns. The lidar curve segments compos-ing the atmospheric noise patterns for training

both CMs were selected from a total of 30864.

The cross-validation error for misdetection per-

centage was calculated by adding together each

SLP misdetection percentage obtained by cross-

validation, because undetected smoke signatures

are discarded and do not pass to the next SLP.

By contrast, atmospheric noise patterns classifiedas smoke signatures pass from one SLP to the

next, so the CM cross-validation error for the false

alarm percentage is the product of the individual

SLPs� false alarm percentages obtained by cross-

validation. The first CM presented cross-valida-

tion error values of 13% for misdetections and

0.0076% for false alarms, while the second CM

led to 33% and 0.011% for misdetections and falsealarms, respectively. The larger percentage of mis-

detections for the second CM compared to the first

one is explained by the greater width and complex-

ity of large-scale fires compared to small bonfire

smoke signatures.

Even though using 10-fold cross-validation and

test sets simultaneously is not a standard proce-

dure, two test sets were used for checking the gen-eralisation ability of the first CM and the

representativeness of the training and validation

sets. It was not possible to build a similar test set

A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632 631

for the second CM due to the insufficient number

of representative smoke signatures from large-

scale fires. For a test set containing 880 small bon-

fire smoke signatures and 29442 atmospheric noise

patterns the first CM presented classification er-rors of 7.4% and 0.041% for misdetections and

for false alarms, respectively. The high classifica-

tion efficiency achieved demonstrates that the 141

small bonfire smoke signatures and 670 atmos-

pheric noise patterns used for training are repre-

sentative of the larger sets of patterns, composed

of 880 smoke signatures from small bonfires and

29442 atmospheric noise patterns, and that it ispossible to use a small number of patterns for

training, an important result because it enables

the sampling required for new CMs to be reduced.

The ability of a CM trained to identify small

bonfire fire signatures to detect large-scale fires

was tested by feeding into the first CM a test set

composed of 232 smoke signatures resulting from

large-scale fires. The CM correctly classified 70%of the patterns. This relatively good performance

is explained by the similarity between peaks re-

corded at the edge of a large fire smoke plume

and those corresponding to small bonfires. The

30% large-scale fire smoke signatures misdetected

by the CM were predominantly recorded at an ad-

vanced stage of the fire (more than 5min after its

start). They failed to be correctly classified becausethe corresponding peaks are broad and present a

complex shape, due to the large size and complex

internal structure of the smoke plume, in particu-

lar when smoke is spread by wind. These facts con-

firm that small bonfire smoke signatures can be

used for training an automatic system for forest

fire detection. Applying the first CM to the classi-

fication of lidar signals obtained by accumulating32 laser pulses at 10Hz laser pulse frequency leads

to about one false alarm every 3.4min. It is possi-

ble to increase the time lapse between false alarms

by checking alarms through the accumulation and

analysis of a new lidar curve in the same direction.

5. Conclusions

Using a method to prevent overfitting based on

ROC curves and the Neyman–Pearson criterion, it

was possible to build a committee machine com-

posed of four single-layer perceptrons that was

optimised for automatic early forest fire detection.

This committee machine was trained using small

bonfire smoke signatures and atmospheric noisepatterns and presented a 93.6% detection efficiency

with only 0.041% of false alarms. It misdetected

30% of smoke signatures from large-scale fires be-

cause the shape of these smoke signatures is very

different from those of the bonfires used to build

the training set. A second committee machine

trained to detect large-scale fires was built, and

led to 33% misdetections and 0.011% false alarms.Both committee machines presented high classifi-

cation efficiency due to the inclusion in the pat-

terns of the parameters peak-to-noise ratio

(PNR), average amplitude ratio (AvAR) and max-

imum amplitude ratio (MAR), which facilitate the

single-layer perceptrons� task of identifying rele-

vant features.

The two CMs developed are able to detect differ-ent types of smoke signatures, so their cooperative

use leads to better detection efficiency. The major

drawback of such a system is the false alarm per-

centage, which corresponds to the union of the false

alarm sets of each CM. It is possible to use only the

first CM when the lidar scanning time is short en-

ough to enable early detection of fire, before the

smoke plume increases and spreads due to wind.Variation of the wavelength, pulse energy, and

divergence of the laser beam used did not noticea-

bly affect the smoke signature shape and, conse-

quently, the CM efficiency. This provides some

flexibility concerning the specification of the lidar,

helping to reduce development costs.

Acknowledgments

A.M. Fernandes gratefully acknowledges PhD

grant SFRH/BD/2943/2000 from Fundacao para

a Ciencia e a Tecnologia. This research is partially

supported by a grant from Agencia de Inovacao,

Portugal (project FOGO!). The authors are grate-

ful to the Portuguese Air Force, to ProfessorXavier Viegas (University of Coimbra), to ADAI

staff and to Mr. Antonio Fernandes for their help

in organising the field experiments.

632 A.M. Fernandes et al. / Pattern Recognition Letters 26 (2005) 625–632

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