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Remote Sensing of Environment 86 (2003) 19–29

Reduction of false alarm rate in automatic forest fire infrared

surveillance systems

S. Briz1, A.J. de Castro*, J.M. Aranda, J. Melendez, F. Lopez

Departamento de Fısica, Universidad Carlos III de Madrid, Avda. Universidad 30, 28911-Leganes, Madrid, Spain

Received 31 May 2001; received in revised form 13 February 2003; accepted 17 February 2003

Abstract

One of the main problems related to infrared remote sensing of forest fires by automatic systems concerns the rejection of false alarms.

The study of the infrared spectral radiance emitted by a biomass fire has been used to define spectral algorithms that permit to separate fires

from other sources considered as false alarms. The different behaviour of the medium (3–5 Am) and thermal (8–12 Am) infrared spectral

regions has been the basis for the definition of a new ‘‘figure’’ of merit, the so-called Fire Index (FI). An experimental burn has been used to

test the ability of FI to identify clearly fires. A second algorithm based on an intraband analysis, the Mid-IR Fire Index (MFI), has been

proposed to give complementary information to FI. Evolution of FI and MFI values with distance for different IR sources has been

calculated. Finally, an experimental validation of both algorithms has been performed by using two infrared cameras working in the medium

and thermal infrared spectral windows.

D 2003 Elsevier Science Inc. All rights reserved.

Keywords: False alarm rate; Fire index; Forest fire surveillance systems

1. Introduction

Fires represent a very important threat for the European

forests, mainly in the Mediterranean area. In Spain, during

the last 5 years, an average of 105 ha per year has been

burned. It has been estimated that fire fighting and restora-

tion cost between 1000 and 5000 euro per burnt hectare. The

European Union has devoted over the last 6 years an

important effort to research projects (Fourth and Fifth

Framework programmes) that cover development of new

technologies for early fire detection, fire danger prediction,

or post-fire rehabilitation and management. Among others,

MEGAFIRES (1996–1998), AFFIRM (1996–1998),

EFAISTOS (1996–1998), FUEGO2 (1998–2001), or

RISCOFF (2000–2001) are good examples of this effort.

A forest fire is a combustion process where temperatures

usually go beyond 400 jC (charcoal undergoes glowing

combustion at around 500–600 jC; thick flames burn

around 1000 jC) and may reach values as high as 1500

0034-4257/03/$ - see front matter D 2003 Elsevier Science Inc. All rights reserv

doi:10.1016/S0034-4257(03)00064-6

* Corresponding author. Fax: +34-91-624-87-49.

E-mail address: decastro@fis.uc3m.es (A.J. de Castro).1 Present address: Departamento de Fısica, Universidad Europea de

Madrid, c/ Tajo s/n, 28670 Villaviciosa de Odon, Madrid, Spain.

jC (Robinson, 1991). According to Planck’s law, a body at

such temperature radiates mostly in the infrared spectral

region. Because of atmospheric attenuation, most IR fire

detection systems are designed to operate in one of the two

atmospheric spectral windows: 3–5 Am (MIR) and 8–12

Am (TIR). Up to now, the satellite sensor most widely used

for the detection of forest fires and/or management of

burned areas is the Advanced Very High Resolution Radio-

meter (AVHRR) aboard the NOAA satellites (Flannigan &

Vonder Haar, 1986). Channel 3 (3.55–3.93 Am) of this

instrument is widely used to monitor forest fires from space.

Casanova (2000) is an example of recent scientific work in

the field of fire management by using the AVHRR images.

Although this sensor has IR channels that can be used to

measure emission from fires, it has not been specifically

designed to monitor fires and it presents various problems:

signal saturation, false alarms, and low efficiency to detect

small burned areas. Some research programmes plan to

launch in the near future space platforms with multispectral

sensors specifically devoted to infrared remote sensing of

forest fires (among other surface hot events):

� small satellite missions like BIRD (Web1, BIR), IRSUTE

(Seguin et al., 1999), or FUEGO (Web2, FUE)� sensors aboard the ISS like FOCUS (Web3, FOC)

ed.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–2920

In contrast with satellite sensors, most ground-based

systems for early detection have been designed to work in

the visible/near infrared spectral range. The basis of these

systems is the identification of smoke in real time by

analyzing its characteristic dynamic and stochastic features

(Kuhrt et al., 2000; Venter, 2000).

However, infrared systems are now being proposed for

automatic ground-based surveillance of forest surfaces

(Ugarte, de Castro, Briz, Aranda, & Lopez, 2000a). For

instance, BOSQUE system developed by Bazan-Faba

(Spain) or BSDS system developed by FISIA (Italy) (Ollero,

Arrue, Martinez, & Murillo, 1999) use high-resolution

infrared thermal cameras to monitor forest areas. Other IR

systems operate aboard an aircraft, like the Firefly sensor,

developed by JPL and USFS (Web4, Fir).

The main problem related to IR remote sensing of fires

by automatic systems concerns the identification of fires and

the rejection of false alarms (sunglints, industrial hotspots,

hot soil due to strong solar irradiance during summer

periods, etc.). There are different approaches in the literature

to the problem, by using spectral information (Pereira &

Setzer, 1993), spatial information (Vries & Kemp, 1994),

signal analysis, or combinations of these methods (Ollero et

al., 1999). The work developed in this paper focuses on the

spectral approach to the problem. In order to design accu-

rately these IR systems, it is very important to have a good

spectral characterization of the infrared emission due to the

fire as well as the other IR emitters that constitute the

Fig. 1. Normalized intensity of spectroscopic lines for

infrared scenario in the field of view of the sensor. In this

paper, this spectral characterization is used to define very

simple algorithms to reject emissions from sources other

than fires. These algorithms could be implemented in an

easy way in simple, rugged, and staring systems specifically

devoted to fire detection. See for instance some work of our

research group on the design of these simple systems

(Ugarte et al., 2000a, 2000b).

2. Infrared emission of a biomass burn

The major products of biomass combustion are CO2 and

H2O, which account for over 90% of the emitted mass. In

order to obtain information on the spectral distribution of

spectroscopic emission lines for both gases, the high-tem-

perature molecular spectroscopic database (HITEMP) has

been used (Web5, HIT). HITEMP is a compilation of the

line-by-line parameters of CO2 and H2O spectroscopic lines.

This database is the high temperature analogous of the

widely used HITRAN spectroscopic database. Fig. 1 shows

the intensity of spectroscopic lines for both gases in the two

spectral windows 2000–3330 cm� 1 (3–5 Am) and 770–

1250 cm� 1 (8–12 Am). Calculations have been performed

by using a temperature of 1000 jC. Only the most abundant

isotope has been taken into consideration. As can be seen in

the figure, CO2 is the most important emitter in the MIR

window. Most of the water lines have intensities below

CO2 and H2O obtained from HITEMP database.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–29 21

10� 20 cm/molecule, whereas carbon dioxide presents a

strong emission band around 2300 cm� 1 with intensities

higher than 10� 19 cm/molecule. Other interesting conclu-

sion is that emission in the TIR region is clearly smaller than

in the MIR region, in particular for CO2. Therefore, the

change due to a fire in the infrared emission of a scenario

will affect mainly the MIR window.

Other sources of infrared radiation in a fire scenario are

the ‘‘solid’’ sources: burning wood, heated soil, embers, and

emitted particulates. All these emitters have a ‘‘greybody’’

behaviour emitting at all the wave numbers with temper-

atures between 230 and 730 jC (Stearns, Zahniser, Kolb, &

Sandford, 1986; Worden, Beer, & Rinsland, 1997).

In order to measure the IR emission of a fire, exper-

imental burns have been performed at the CIFOR-INIA

facilities (Spanish Ministry of Science and Technology).

The heating area is a tunnel (8.5-m length) composed of

Fig. 2. Top: scheme of the experimental setup for controlled burns. Bottom:

eight small wagons where the forest fuel is spread. Wind

speeds between 1 and 7 m s� 1 are simulated by using a fan

located at the tunnel entrance. A Fourier transform infrared

(FTIR) spectroradiometer was located on top of the fan

structure with its line of sight pointing to the sixth wagon

with a zenith angle of h = 108 jC. With this configuration,

both the flame and the soil emission were measured by the

instrument. A scheme of the experimental setup is shown in

Fig. 2 (top). The spectroradiometer is equipped with a

mercury–cadmium–telluride (MCT) detector and measures

the spectral radiance between 600 and 4500 cm� 1 (c 2.2–

16.7 Am). This instrument has been previously calibrated to

radiometric units W/(cm2 sr cm� 1) by using a blackbody at

two known temperatures (Revercomb et al., 1988). Fig. 2

(bottom) shows a typical spectrum measured during a straw

burn compared with the one corresponding to unburned

terrain. Theoretical considerations about the expected signal

typical distribution of spectral radiance measured during a straw burn.

-0.5

0.0

0.5

1.0765

Fire

inde

x

bb 500 °C

sunglint

straw burn

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–2922

made above are clearly evidenced in the experiment. In the

MIR region, the CO2 emission band is the main feature.

This band appears like two ‘‘peaks’’ (the so-called red and

blue spikes; Wormhoudt, 1985) due to atmospheric carbon

dioxide absorption. Water emission is also present, although

its contribution to the radiance is noticeably smaller. Emis-

sion of the hot soil appears as a continuum at all the wave

numbers (a blackbody-like contribution). In contrast with

the MIR region, the TIR region shows no important gas-

related emission features.

0 100 200 300 400 500 600 700 800-1.0

Time (s)

bb 100 °Cbb 27 °C

Fig. 3. Evolution of FI values during a straw burn. Horizontal dashed lines

correspond to the FI value calculated for different blackbody temperatures.

Vertical dashed lines indicate the beginning of fire in wagons #5, 6, and 7.

3. Spectral interband analysis: the fire index algorithm

The approach proposed in this paper is the definition of a

new ‘‘figure of merit’’ based on the different behaviour of

MIR and TIR spectral regions. As it has been shown above,

emission in the MIR region is strongly modified by the fire

as compared to the unburned situation. This change is

mainly due to the spectral features of CO2 emission. In

order to define the fire index, two ‘‘channels’’ will be

specified: one into the MIR spectral window, from 2000

to 2410 cm� 1 (4.15–5 Am), and another into the TIR

region, from 800 to 1100 cm� 1 (9.09–12.5 Am). The first

channel takes account of the emission of the red and blue

CO2 spikes. The figure of merit is defined by a comparison

of both channels. By analogy with the definition of the

vegetation indexes (Holben, 1986), a fire index FI is defined

as follows

FI ¼ RM � RT

RM þ RT

ð1Þ

where RM and RT are the radiances integrated in the channels

defined into the MIR and TIR regions, respectively. RM is

very sensitive to the CO2 red spike emission, whereas RTwill be much less sensitive to the emission of hot gases. This

different behaviour of RM and RT makes Eq. (1) a promising

way to identify fire events in a scene.

In order to check the behaviour of the fire index during a

fire, a straw burn has been performed in the tunnel. The

spectroradiometer was programmed to acquire radiance

spectra at each 5 s. Fig. 3 shows the evolution during this

burn of the FI values calculated from spectra measured by

the spectroradiometer. Horizontal dashed lines mark the FI

values calculated for blackbodies at different interesting

temperatures. At the beginning, unburned soil gives FI

values around � 1. When the burn begins, FI increases.

This increase is due to the smoke column produced in the

first wagons, which ascends and intercepts the field of view

(FOV) of the spectroradiometer. This smoke column con-

tains CO2 relatively cold, which increases slightly the value

of RM, whereas RT remains practically unchanged. Vertical

dashed lines in Fig. 3 indicate the beginning of fire in the

corresponding wagon (only wagons #5, 6, and 7 are

indicated for clarity purposes). When fire spreads over the

wagons, the CO2 in the smoke column that intercepts the

spectroradiometer FOV is hotter. That means a stronger red

spike in the emission, and that FI continues increasing.

Maximum values of FI (around 0.7) are reached when

wagon 6 is burning. At this moment, flames (the hottest

CO2) are inside the FOV. After this moment, the postcom-

bustion process is observed. Flames are less intense and do

not fill completely the FOV, and FI begins to decrease due

to the decrease in the red spike intensity. During this

process, short peaks appear in the FI value (for instance,

at 495 and 515 s), associated to combustions reactivated by

the wind. After t= 600 s, there are no more oscillations, as

corresponds to a situation where only the embers are

emitting infrared radiation.

It is important to compare the FI values reached by the

fire emission with those corresponding to high-temperature

blackbodies (horizontal dashed lines in Fig. 3). As can be

seen, temperatures as high as 500 jC give FI values clearly

below those corresponding to the fire in wagon 6. Sunglints

have been simulated by supposing a 5727 jC blackbody

emission, corrected by atmospheric attenuation, and

reflected by a surface with maximum reflexivity of unity.

Atmospheric transmittance has been calculated by using the

PcModWin3 code (Web6, MOD). This code is a PC version

of the well-known MODTRAN atmospheric code. FI values

associated to sunglints are around 0.35, and they are also

well separated from the fire. In conclusion, FI seems to be a

good figure of merit to discriminate high-temperature events

not associated to the fire (false alarms for an automatic

surveillance sensor system).

Efficiency of FI to identify fire events is directly asso-

ciated to the intensity of the CO2 red spike, which depends

not only on the CO2 concentration and temperature, but also

on the atmospheric absorption. That means that the spike

intensity, and in consequence, the FI value, depends on the

source-detector distance. Experimental measurements on the

tunnel involve distances around 10 m between the sixth

wagon and the spectroradiometer. This distance is appro-

Fig. 4. Evolution of FI value with distance calculated for fire, blackbody temperatures of interest, and sunglints.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–29 23

priate for the radiometric characterization, but it is obviously

not realistic for a surveillance system. Values of FI for

different distances have been calculated by correcting the

experimental radiance of fire (measured at wagon 6) with

the atmospheric absorbance calculated by MODTRAN. The

1976 US Standard Atmosphere model has been used in the

calculations, with a CO2 mixing ratio of 350 ppm V. In order

to take into account scattering effects, the Rural-VIS = 23 km

aerosol model has been included. Fig. 4 illustrates the

variations of FI with the source-detector distance. As can

be seen, FI decreases with the distance because atmospheric

attenuation affects mainly the MIR region, reducing the

intensity of the red spike (RM diminishes faster than RT).

Fig. 5. Calculated evolution of FI value with the FOV degree of fill

The calculated FI for 100, 500, and 700 jC blackbodies are

also included in the figure, as well as a simulated sunglint

for comparison purposes. Separation of fires and black-

bodies as hot as 500 jC is clearly maintained up to 10 km of

distance. Discrimination with hotter sources (700 jC) can bemaintained up to a distance of 1 km. However, the FI fails to

reject pure sunglints for distances longer than 100 m. The FI

algorithm must be enhanced with other criteria that comple-

ment the alarm decision system to overcome this short-

coming.

Other aspect that needs to be studied is the degree of

filling of the FOV. Sometimes, the fire event does not fill

completely the FOVof the sensor but only a percentage that

ing for fire, blackbody temperatures of interest, and sunglints.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–2924

we will call ‘‘FOV fill factor’’. From the sensor point of

view, the observed scene will be composed of a high-

temperature event against a cold background. In Fig. 5, FI

values for different FOV fill factors have been simulated by

composing a scene with a 27 jC background and a hot

blackbody. Calculations have been performed for a distance

of 1 km. Fire is the most affected event by the variation of

the fill factor. As can be seen, fires are very well distin-

guished from blackbodies around 100 jC for any fill factor.

However, blackbodies at 500 jC are well separated only for

fill factors above 0.4. At the studied distance, sunglints have

always higher FI values than fires, and they are not

separated from fire for any value of the fill factor.

4. Spectral intraband analysis: the mid-IR fire index

algorithm

The fire index FI defined in Eq. (1) uses two channels at

the MIR and TIR spectral regions. It is also possible to

define another index using only one channel at the mid-

infrared spectral region. It can be noted from Fig. 2 that

spectral features of CO2 emission (the red and blue spikes)

are limited to a narrow window within the MIR region. This

characteristic can be used to define a new spectral index on

the same basis of Eq. (1). Two channels RM1 and RM2 are

defined: RM1 from 2100 to 2410 cm� 1 (4.15–4.76 Am) and

RM2 from 2410 to 3000 cm� 1 (3.33–4.15 Am). RM1 is the

specific region that takes into account the CO2 emission,

analogous to RM channel in Eq. (1), whereas RM2 is a wide

channel that covers the spectral window in the MIR region

that does not present emission features corresponding to the

hot gases.

Fig. 6. Evolution of MFI values during a straw burn. Horizontal dashed lines c

Vertical dashed lines indicate the beginning of fire in wagons #5, 6, and 7.

In this way, the mid-IR fire index MFI is defined as

MFI ¼ RM1 � fRM2

RM1 þ fRM2

ð2Þ

Factor f is included to take into account in the algorithm the

different spectral width of RM1 and RM2 channels. In our

case, f = 0.53.

Fig. 6 illustrates the behaviour of the MFI during the

straw burn. MFI has a value of 0.6 for the unburned

terrain. When the hot gases intercept the spectroradiometer

FOV, MFI increases due to the larger emission in the RM1

channel, reaching values as high as 0.9 during the burn of

wagon #6. However, it is observed by comparison with

Fig. 3 that MFI is not so effective as FI to distinguish

between different phases of the fire spread. MFI reaches

the higher value not only during the burn of wagon #6, but

also during the first stages of the burn. In addition, MFI

values range from 0.6 (unburned condition) to 0.9 (strong

fire), whereas the FI has a wider range (� 1 to 0.7). On

the other hand, this index has an advantage in its behav-

iour for high-temperature blackbodies. Horizontal lines in

Fig. 6 show the MFI value for different blackbody temper-

atures, as well as for sunglints. As can be seen, MFI

decreases when temperature increases, with the smaller

value corresponding to the higher temperatures. Sunglints

show the smallest values of the index (around � 0.7). The

trend of MFI with increasing temperature is opposite for

gas emissions (towards greater values than the unburned

reference) than for blackbody emissions (towards smaller

values than the unburned reference). This behaviour would

permit to separate high-temperature sources and sunglints

from fires even at long distances, where FI fails to reject

orrespond to the FI value calculated for different blackbody temperatures.

Fig. 7. Evolution of MFI value with distance calculated for fire, blackbody temperatures of interest, and sunglints.

Fig. 8. Top: infrared MIR image of a fire. Bottom: radiance profiles corresponding to three selected heights.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–29 25

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–2926

these false alarms. Fig. 7 represents values of MFI for

different source-detector distances calculated with the same

methodology used for Fig. 5. It is very interesting to

observe that, although the MFI value decreases with the

distance, fire is well distinguished from the high-temper-

ature blackbodies. At 10 km, sunglints are still perfectly

separated from fires. However, MFI separates worse than

FI blackbodies with temperatures below 100 jC. The main

conclusion is that MFI gives complementary information

to the FI to separate false alarms from real fires. This

separation can be effective in the wide-range 100 jCsunglints.

5. Experimental validation of the algorithms

Up to this point, the discussion has been based upon

measurements performed with an FTIR spectroradiometer.

Fig. 9. Top: FI image of the same scene shown in Fig. 10. Bottom: FI hor

This instrument has a unique combination of time and

spectral resolution capabilities, but it is not rugged enough

for routine field measurements. In addition, real forest fire

remote sensing applications demand some degree of spatial

resolution. Therefore, after the indexes have been proposed,

it is advisable to validate them experimentally and to assess

its applicability to real fires by using an imaging instrument,

as rugged and simple as possible.

Within the framework of the UE-DGXII Project

FUEGO2: Instrument Design Prototype Construction and

Validation, an image acquisition system prototype has been

set up and used to perform fire measurements (Aranda, Briz,

Melendez, de Castro, & Lopez, 2000). The acquisition

system is composed of two infrared cameras in the MIR

and TIR bands. Two basic requirements have been imple-

mented: capability to obtain digital images and sequences

and simultaneous acquisition by both cameras. The cameras

have been mounted on a platform in order to align their

izontal profiles corresponding to the same heights shown in Fig. 10.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–29 27

optical axes. A computer, provided with two digital frame

grabbers and specific software, controls the acquisition.

Both cameras have been calibrated in our laboratory against

a blackbody. By using neutral density filters and dia-

phragms, a wide temperature range of calibration (0–1000

jC) has been achieved. The simultaneous MIR and TIR

images obtained by this system can be subsequently co-

registered in order to have a correspondence between pixels

in both images.

The system described above is the ideal tool to image in

quasi-real time the FI algorithm because of the simulta-

neity in the MIR and TIR image acquisition and the co-

registration of the images. These properties are of funda-

mental importance for any algorithm to be applied to

sources like flames, which are neither stationary in time

nor homogeneous in space. A simplified version of the FI

index has been implemented because the MIR and TIR

cameras have no spectral filters to match the optimum RM

and RT spectral ranges. The full 3–5 and 8–12 Am ranges

have been used instead.

Fig. 10. Top: infrared MIR image corresponding to a sunglint (source #1) and a la

level of both sources.

In order to investigate the behaviour of the FI algorithm

in a real scenario, a fire using wood as fuel has been

measured. For security purposes, the fire has been made

inside a circular metallic container with a diameter of 0.70

m. Measurements were performed at a distance of 30 m.

Fig. 8 corresponds to the radiometric MIR image of the fire.

A graph with three selected horizontal profiles has also been

included. These profiles compare the radiance level coming

from the metallic container and the signal coming from two

heights in the flame (140 and 92 cm over the top surface).

As can be seen, radiance from the container is around four

times the value of the radiance on the flame at 140 cm over

the surface. An alarm level based only on the radiance in

the MIR image would reject the ‘‘coldest’’ region of the

flame (probably the most interesting region for an early

detection of the fire). Eq. (1) has been applied pixel by pixel

to the MIR and TIR images to obtain an FI image of the

scenario. Fig. 9 shows this FI image and the same three

horizontal profiles for FI values. In this image, the flame top

can be very well distinguished from the metallic container in

boratory flame (source #2). Bottom: horizontal profile showing the radiance

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–2928

spite of its lower radiance level. FI images demonstrate their

ability to separate flames from other sources even for very

low flame radiance levels.

As was pointed above, MFI algorithm is very interesting

because of its capability to separate very high signal levels

coming from sunglints or very hot sources from signals

coming from a fire. In order to validate the MFI algorithm,

only the MIR camera and two optical filters to define the

RM1 and RM2 channels will be needed. In order to obtain a

rugger system, some simplifications in the spectral defini-

tion of RM1 and RM2 channels have been done. The MIR

camera used in the experiment works in the 3–5 Am spectral

region when no filters are used. This whole range includes

the CO2 emission peaks and also all the contributions

coming from other sources (solid hot bodies and sunglints)

in the scenario. In this sense, the whole range from 3 to 5

Am can be used as channel RM2. In this way, we avoid the

use of one optical filter in the system. A commercial high-

pass spectral filter with a cutoff wavelength at 4.2 Am has

been used to define the RM1 channel because this filter

Fig. 11. Top: MFI image of the same scene shown in Fig. 10. Bottom:

removes most of the contribution of the other sources than

the flame.

In order to check the efficiency of the MFI algorithm, an

artificial scenario composed by two different infrared sour-

ces has been made (source #1: sunglint; source #2: flame).

Fig. 10 (top) shows the MIR image of this scenario. As can

be seen in Fig. 10 (bottom), the radiance level correspond-

ing to the sunglint is one order of magnitude higher than the

one corresponding to the flame. From this image alone, it

would be completely impossible to identify the sunglint as a

false alarm.

The image corresponding to the top of Fig. 10 will be

used as the channel RM2 defined in the MFI algorithm. A

second image, corresponding to the same scenario, was

acquired by using the spectral high-pass filter at 4.2 Am.

This second image will be used as the channel RM1. Fig. 11

(top) shows the image corresponding to the application of

MFI (Eq. (2)) on the images with and without the spectral

filter. Fig. 11 (bottom) corresponds to the MFI profiles for

sources #1 and #2. Despite of its larger MIR signal, the

horizontal profile showing the MFI values of sources #1 and #2.

S. Briz et al. / Remote Sensing of Environment 86 (2003) 19–29 29

sunglint is very well separated from the flame signal. MFI

value of the sunglint tends towards � 1, whereas the

corresponding value of the flame tends towards + 1, as

was pointed in Section 4. From this image, source #1 was

clearly identified as a false alarm, and source #2 was

identified as a fire. The image of Fig. 11 illustrates how

the MFI algorithm can be used to discriminate false alarms

in forest fire surveillance systems.

As was pointed out in Section 1, most future smart

surveillance systems of hot events are planned to be based

on IR sensors. As an example, the German satellite BIRD

(Web1, BIR), launched on October 2001, has channels in

the visible/near infrared, MIR, and TIR spectral bands. The

satellite constellation planned in the FUEGO project (Web2,

FUE) will also use MIR and TIR spectral bands.

The results obtained with our image acquisition system

demonstrate that fire index imaging can be implemented

by using ordinary IR cameras with only relatively minor

modifications, thereby making possible to apply bi-spec-

tral algorithms to ground- or airborne-based detection.

The results and implementation described can be of

interest to provide ‘‘ground-truth’’ validation to the satel-

lite systems.

In addition, the results described in this section, in

particular, in combination with those presented in Section

4 for the intraband analysis, provide some orientation for

the design of ground-based IR forest fire surveillance

systems.

6. Conclusions

In this paper, very simple algorithms working in the 3–5

and 8–12 Am spectral regions have been proposed to be

incorporated in an automatic smart surveillance system. A

study of the behaviour of these algorithms in reference to

the fire distance and FOV degree of filling has been

performed. The combination of inter- (MIR-TIR) and intra-

(MIR) band analysis appears to be the best solution to obtain

an effective rejection of false alarms in the system. Finally,

an infrared image acquisition system measuring in both

MIR and TIR channels has been used to prove the ability of

the FI and MFI algorithms to discriminate false alarms in the

scenario under surveillance.

Acknowledgements

The authors wish to thank C. Hernando, M. Guijarro, and

C. Dıaz (Instituto Nacional de Investigaciones Agrarias,

Spain) for the performance of the controlled burn and their

scientific comments. One of the authors (S.B.) acknowl-

edges financial support from the Spanish Ministerio de

Educacion y Cultura.

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