A SIMPLE NEURAL-NETWORK ALGORITHM

FOR CLASSIFICATION OF LIDAR SIGNALS APPLIED

TO FOREST-FIRE DETECTION

Andrei B. Utkin, Alexander Lavrov

INOV - INESC Inovação, Rua Alves Redol, Lisbon, Portugal

Rui Vilar

DEMAT, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal

Keywords: Perceptron, Lidar, Signal processing, Singular value decomposition, Radial-basis function networks.

Abstract: Detection of smoke plumes using lidar provides many advantages with respect to passive methods of fire

surveillance. However, the great sensitivity of the method results in the detection of many spurious signals.

Correspondingly, the automatic lidar surveillance must be provided with effective algorithms of separation

of the smoke-plume signatures from irrelevant signals. The paper discusses a simple and robust lidar pattern

recognition procedure based on the fast extraction of sufficiently pronounced signal peaks and their

classification with a perceptron, whose efficiency is enhanced by a fast nonlinear preprocessing. The

algorithm is benchmarked against previously developed artificial-intelligence methods of smoke recognition

via Relative Operating Characteristic (ROC curve) analysis.

1 INTRODUCTION

Extending the principles of radar to the optical

range, lidar (light detection and ranging) presents

significant advantages in comparison with passive

surveillance methods, in particular a higher

sensitivity and low dependency on light and weather

conditions. Lidar detectors provide a large range of

surveillance, restricted only by the laser-pulse

energy and — for distances exceeding ~10 km — by

the beam jitter resulted from atmospheric turbulence.

Good directionality and precision of distance

measurements enable lidar to provide an accurate

location of smoke plumes. For efficient early forest-

fire detection, the smoke-plume pattern in the lidar

signal (peak of the retroreflected power) must be

promptly recognized by an adequate automatic

procedure despite the presence of additional peaks

due to noise and other targets. The present paper

details the investigation of one such procedure based

on the fast localization of peaks whose amplitude is

sufficiently large to correspond to possible smoke-

plume signatures. These peaks are subjected to

feature extraction and highly nonlinear binarization

transformation, which increases the number of signal

components. The binarized patterns are then

classified with a single-layer perceptron.

Lidar equipment (Fig. 1) consists of a radiation

emitter (pulsed laser and beam-formation optics) and

a radiation receiver (usually comprising of a light

gathering optical train, photodetector and

preamplifier). The emitter produces short and

intense radiation pulses; a part of this radiation is

scattered backwards and collected by the receiver,

where its power is converted into an electric signal.

The electric signal is amplified and directed to the

data-acquisition unit, to be recorded in a digital form

as a function of time.

Figure 1: Lidar equipment and detection principles.

569

Utkin A., Lavrov A. and Vilar R. (2009).

A SIMPLE NEURAL-NETWORK ALGORITHM FOR CLASSIFICATION OF LIDAR SIGNALS APPLIED TO FOREST-FIRE DETECTION.

In Proceedings of the International Joint Conference on Computational Intelligence, pages 569-574

DOI: 10.5220/0002334305690574

 SciTePress

Lidars for automated surveillance are supplemented

with a signal recognition system, performing

classification of target signatures and issuing, if

needed, an alarm signal containing information

about the target that caused the alarm situation.

2 BASIC RELATIONS

The distance from the lidar to the target R may be

calculated from the time delay t between the laser-

pulse emission and the reception of the

backscattered signal,

cRt /2= , c is the velocity of

light. The raw lidar signal S is the receiver-unit

output voltage recorded during some period of time

immediately after the laser-pulse emission (

t ).

As far as the transition from the time to the distance

dependence is reduced to a simple rescaling, usually

the raw lidar signal is represented as a plot of S

versus the distance R rather than the time t:

() ( )

(

)

2/,

ctRSRGIRStS

ubph

=+=⇒

(1)

where G is the total electronic gain,

() ()

RPRI

rphubph

is the unbiased photodetector

current (

is the photodetector responsivity and

P is the retroreflected radiation power) and

S is

the background component, accumulating all types

of electric displacement and low-frequency noise

that can be assumed to be constant during the

relatively short measurement time: about 67

μs for a

range of 10 km, according to relation (1).

A theoretical estimation of

P is given by the

lidar equation:

()

⎟

⎠

⎞

⎜

⎝

⎛

−=

∫

rectr

rec

dRR

ERP

''2exp

αττ

(2)

where

E is the output laser pulse energy,

the

backscattering coefficient of the medium,

rec

the

effective receiver area,

and

rec

the transmitter

and receiver efficiencies, and

the extinction

coefficient.

At the early stage of a fire, the characteristic

spread of the smoke plume in the direction of laser-

beam propagation

RΔ

(Fig. 2) is about 10 m. To

be able to reveal specific few-meter scale structures

that make the smoke-plume signatures different

from other lidar returns, the data-acquisition unit

must measure the photodetector output with a

sampling interval

~ 1.5 m, eventually yielding

the discrete-time lidar signal in the form

()()

(

)

()

,''2exp

constA

EGC

SdRR

CtRS

rectrreclph

⎟

⎠

⎞

⎜

⎝

⎛

−=

∫

ττξ

(3)

digitized at the points ,/2 cRt

= ,RiR

RRiii

/,,,1,0

maxmaxmax

… .

Figure 2: The main spatial parameters of smoke-plume

detection.

According to (1) and (2), smoke plumes manifest

themselves in raw lidar signals as peaks whose

amplitude and shape vary due to the stochastic

changes in the particle distribution within the smoke

plume under the action of gas-dynamic forces,

buoyancy and wind. The smoke-plume signatures

are observed against a background contaminated by

electronic and atmospheric noise (Fig. 3). Electronic

noise of a well constructed receiver usually

demonstrates no dependence on the distance and can

be estimated from a signal segment recorded far

beyond the range of the instrument, where no signal

attributable to retroreflection is expected.

Figure 3: Composition of the raw lidar signal.

Apart from this unstructured noise, the lidar signal

may contain peaks due to retroreflection from hills,

trees, buildings, birds, etc. Solid-target signatures

Smoke

plume

signature

Peaks due to atmospheric and

electronic noise

R, m

Lidar signal, a.u.

Irrelevant

target

IJCCI 2009 - International Joint Conference on Computational Intelligence

570

are narrow pulse-like waveforms, since the

backscattering occurs at almost a single distance.

The shape of these peaks is mainly defined by the

bandwidth of the detection channel and the rate of

the analog-to-digital conversion.

3 RECOGNITION STRATEGY

3.1 Characterization

As seen from (3), the shape of the smoke-plume

signatures in the lidar signal depends in a

complicated way on the profiles of the extinction

and backscattering coefficients along the beam

propagation direction. Although important for

prediction of the lidar range, gas-dynamic smoke-

plume models do not provide a solid basis for the

extraction of the characteristic features of the

smoke-plume signature. Due to this lack of reliable

parametric models, automated fire surveillance is

mainly based on artificial-intelligence algorithms

such as neural network (NN) methods.

In principle, lidar identifies targets with the

precision of a few meters, thus allowing for a very

accurate location of the fire. The angular target

position (the azimuth

and elevation

, see Fig. 2)

is given by the laser beam direction, but the

calculation of the distance to the smoke plume R

carried out by the signal analysis unit.

NN architectures and algorithms suited for lidar

data extraction have been discussed in the literature

since the 1990s (Bhattacharya et al., 1997). It was

established that waveforms containing small

retroreflection from distributed targets could not be

directly presented to a neural network. A simple and

fast preprocessing method was developed for

facilitating the recognition, ensuring, at the same

time, that the processed waveforms properly reflect

subtle variations in the original waveforms.

Following the same principles as the radial-basis

function algorithms (Bishop, 1995; Haykin, 1999),

the recognition efficiency of a perceptron-based NN

is enhanced by a special binarization procedure that

uses a 2D grid in the signal-distance plane for the

waveform representation and a point-to-node

proximity criterion for assigning one or zero to the

grid nodes. Each node is treated as a separate input

component, increasing the network input dimension,

number of adjustable weights and, according to

Cover's theorem (Haykin, 1999), improving pattern

separability.

3.2 Problems

The application in question is characterized by the

following difficulties:

1. The length of the discrete-time sequence to be

processed,

RRi

maxmax

~6.7×10

, is much

larger than in other lidar applications, such as

underwater object detection (Mitra et al., 2003). As a

result, the conventional NN algorithms

(Bhattacharya et al., 1997; Mitra et al., 2003) cannot

be straightforwardly applied because they require

excessive computation time and resources. In

addition, fire may occur anywhere within the

surveillance range, so no narrower region of interest

can be selected a priori.

2. Smoke-plume signatures are compact. As seen

from Eq. (3), for a starting fire the characteristic

spread of a smoke-plume signature

RΔ , within

which the backscattering factor

is sufficiently

large to produce the signal above the noise level, is

restricted by the spread of the plume:

≈

≤

spss

RR m. Well-developed fires result in

much wider plumes, but denser smoke increases the

laser-beam extinction up to the values

~ 0.2 m

–1

(Kozlov and Panchenko, 1996). In these

circumstances, the smoke-plume signature decreases

down to the noise level at distances of the order of

–1

due to the Beer-Lambert absorption of both the

laser beam and retroreflected light, resulting in

~ 5 m. Measured as number of points in the

digitized signal,

RRN

ssss

, the signature

spread is always much less than that for the cases

described by Bhattacharya et al. (1997) and Mitra et

al. (2003), typically consisting of 5-10 points. The

short signature width and the great variety of

possible waveforms impede application of statistics-

based algorithms for noise reduction and signal

compression, which effectively reduce the

computational load in many other applications

(Mitra et al., 2003).

3. The fact that the distance to the target R

must

be determined during the recognition may

complicate the NN structure: for the straightforward

algorithms, it turns the multiple input - single output

classification scheme into one with multiple outputs,

in which the additional neurons codify, in an analog

or digital way, the value of R

sp.

4. Due to the fact that a constant background can

be represented as a sum of uniformly distributed

peaks, the problem of peak recognition is not

linearly separable a priory and cannot be solved

without introduction of preprocessing and/or non-

linearity.

A SIMPLE NEURAL-NETWORK ALGORITHM FOR CLASSIFICATION OF LIDAR SIGNALS APPLIED TO

FOREST-FIRE DETECTION

571

3.3 Knowledge and Invariances

According to general indications (Anderson, 1988),

to solve the problems presented in Sec. 3.2, a

specialized NN algorithm must be developed,

incorporating all prior information in order to

simplify the overall structure and facilitate the

recognition. Depending on its nature, the knowledge

about the input signal can be represented as a

transformation, selection rule and/or invariant and

then built into the system via specific design or

preprocessing procedures (Haykin, 1999).

The analysis of the lidar signal, briefly presented

in subsections 2.1, 2.2 and 3.2, makes it possible to

point out the following peculiarities:

1. The smoke-plume signatures manifest

themselves in the raw lidar signal as peaks whose

characteristic width

RΔ (several meters) is much

less than the typical distance to the smoke plume R

(from hundred meters to several kilometers).

2. The position of the smoke-signature maximum

corresponds to the desired distance to the smoke

plume.

3. The local noise level may be estimated as the

root-mean-square of the signal just before and after

the peak and the segment of the lidar signal of the

length ~

RΔ3 , containing the smoke-signature

maximum in its center, is supposed to provide

information of both the smoke-signature shape and

the local noise. The ratio of the peak amplitude to

the mean local noise, called peak-to-noise ratio

(PNR), represents an important scale-independent

characteristic of the peak magnitude, closely linked

with the probability of the peak to be a target

signature rather than clutter. For this reason, it is

worthwhile to treat PNR as an invariant

characteristic feature to be extracted and presented

for recognition in a separate way.

Within the range

max

10 RRR

≤≤Δ the shape

factor of the smoke-plume signature is invariant with

respect to the distance R (Utkin et al., 2009).

Obviously, the noise distorts the smoke-plume

signatures more at greater distances, and the pattern-

recognition problem in question can be treated as

distance-independent in the sense that the

recognition conditions for a tenuous smoke plume

are equivalent to those for a dense plume observed at

a greater distance provided that the signal-to-noise

ratio is the same.

3.4 Implementation

The knowledge and invariances are built into the

system via the following preprocessing procedure:

The raw lidar signal consisting of several thousand

points (

RRi

maxmax

) is viewed by the

preprocessing software through a window of several

tens of points (

/3~

) that moves along the

signal curve. The window motion stops if the local

signal maximum coincides with the window center

R and the corresponding peak-to-noise ratio

)(

RPNR is calculated. If

()

thrw

PNRRPNR < ,

where the threshold value

thr

PNR (typically, from 3

to 5) is chosen in accordance with the sensitivity of a

given lidar system, the peak is considered to be too

small for being a smoke-plume signature and the

observation window continues its motion along the

lidar-signal curve. Otherwise the signal pattern

within the window is sent to the recognition unit.

The corresponding feature value

)(

RPNR is

introduced directly to the NN through a special input

(Fig. 4).

Eventual alarm generation is performed on the

basis of pattern classification (smoke-signature

dichotomy) with a single-layer neural network

(perceptron), which is functionally equivalent to the

adaptive linear filter (Haykin, 1999).

Figure 4: Stages of the smoke-signature recognition

procedure.

PNR calculation

=1296 m

NR = 2.485

Pattern extraction

=1584 m

NR = 6.745

PNR

6.745

–

PNR

Threshold

binarization

Perceptron =

linear least-squares

filtering

Pattern recognition

Initial input: raw lidar signal

Bias +1

IJCCI 2009 - International Joint Conference on Computational Intelligence

572

Borrowing the approach from the radial-basis

function network theory (notably, it can be shown

that the binarization processing correspond to a

radial-basis function technique with some specific

norm), classification efficiency of the perceptron is

enhanced by nonlinear threshold binarization

transformation to higher dimensional space, similar

to Bhattacharya's processing of lidar signal

(Bhattacharya et al., 1997) for the detection of fish

in near-shore waters: The signal pattern is mapped

on a rectangular 2D grid. Each sample point is

checked against the grid-crossing points. If a sample

point falls within half a grid space on either side in

both the horizontal and vertical directions, a one is

assigned at that point; otherwise, a zero is assigned.

Each sample point is tested in the same way,

yielding at the end a matrix of zeros and ones,

eventually converted into a longer binary pattern

vector reflecting all the peculiarities of the pattern,

provided that a sufficiently fine grid is chosen.

When the vertical grid spacing equals the lidar

sampling distance so that all the signal points are

located on the vertical grid lines, the above

algorithm reduces to a simple point binarization of

the signal with resolution corresponding to the

horizontal grid spacing. The threshold binarization

procedure, corresponding to the point binarization in

which a one is assigned to each grid point situated

below any point already assigned to one, is even

easier for hardware implementation (a batch of

threshold detectors with linearly increasing

thresholds) and results in less sparse and more

compact binarized samples: the bottom line always

contains ones and can be discarded.

Notably, the number of binarization levels is a

free parameter of the recognition scheme.

Decreasing the number of levels, one can produce

rougher binary signal description and, at the same

time, reduce the signal dimension and the number of

adaptive parameters of the recognition process.

Being chosen on the basis of the bias-variance trade-

off (Haykin, 1999), the number of binarization levels

plays the same role as the number of training epochs

in the iterative learning rules.

The supervised learning procedure is implemented

through the least-squares filtering. For a given

training set, it readily yields a unique deterministic

solution (Haykin, 1999) for the desired

interconnection weights as a product of

pseudoinverse of the matrix composed from the

binarized training samples and the vector of

corresponding classification tags (here, 1 for the

smoke-signature peaks and –1 for the spurious signal

peaks).

Following Bishop's recommendations (Bishop,

1995), the instability arisen from the sparse nature of

the binary-sample matrices and incomplete ranks is

overcome by stabilized pseudoinversion on the basis

of singular-value decomposition (Press et al., 1986).

The binary input has two additional entries: one for

the constant activation bias (+1) and the other for the

PNR value that passes to the perceptron without

binarization. The alarm signal is accompanied by the

current position of the moving window center

spw

RR ≈

that corresponds to the maximum of the

retroreflected radiation and thus provides the desired

distance to detected smoke plume.

Figure 5: ROC curves corresponding to the developed

threshold-binarization algorithm and the three committee

machines described by Fernandes et al. (2004).

4 RESULTS

Fig. 5 illustrates comparison of the developed

threshold-binarization algorithm with three more

complicated artificial-intelligence methods

developed for smoke-signature recognition in the

lidar signal (Fernandes et al., 2004). The threshold-

binarization algorithm demonstrated superior

efficiency in the area of false alarm rate greater than

0.65%, resulting to 100% detection of the smoke

signatures in the validation set at the false alarm rate

as low as 0.84% (91 false detections at recognition

of 10891 noise peaks in 112 recorded lidar signals).

At the same time, the proposed algorithm:

 yields nearly one order of magnitude faster

training;

 the developed supervised learning procedure is

not connected with the choice of the best

classifier, so it can be strictly formalized and

performed by users without special instruction;

A SIMPLE NEURAL-NETWORK ALGORITHM FOR CLASSIFICATION OF LIDAR SIGNALS APPLIED TO

FOREST-FIRE DETECTION

573

 the learning procedure is fast and of predictable

duration: it does not involve repetitive/iterative

routines like training epochs in the case of

gradient-descent methods and

 the global minimum of the classification error

for given training set is readily achieved by a

sequence of matrix operations of guaranteed

stability.

At very close distances to the lidar,

≤ 10 , the

shape factor of the smoke-plume signature does

depend on

. However, it was observed that the

shape distortion does not affect the recognition

capability of the system for this region, mostly due

to very high PNR feature value that activates the

alarm output even if the input from the binary-

sample nodes is not univocal.

5 CONCLUSIONS AND FUTURE

WORK

The neural-network algorithm in question is

extremely flexible; it was successfully used for

automated signal processing in a variety of lidars,

including a system for forest fire surveillance

already deployed in Central Portugal within the

framework of the AGRO project supported by

Portuguese Ministry of Agriculture.

As compared to alternative methods of

automated fire detection, which mostly focus on

radiometry and video/infrared imaging (San-Miguel-

Ayanz et al., 2005), the present active technique, due

to its potentially higher sensitivity, offers quicker

response to the alarm situation. In addition,

automation of the 1D lidar signal processing is an

easier task than fire or smoke-plume recognition in

the 2D images provided by video/infrared cameras.

Future developments of the described algorithm

are connected with invoking additional information

extracted from statistical properties of the collected

lidar returns.

ACKNOWLEDGEMENTS

This research has been supported in part by Agência

de Inovação (AdI) within the framework of POCI

2010 program (FEDER funded), project SIDAI.

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