We present a method for early forest fire detection from a satellite image using the belonging probability matrix image. We have considered each satellite image matrix line as a realization of a nonstationary random process in the thermal infra-red (TIR) spectral band and then divided each line into very small stationary and ergodic intervals to obtain an adequate mathematical model. Furthermore, the pixels of the satellite image are considered to be statistically independent; thus, any small interval of each line behaves, naturally, as a Gaussian stationary noise. In this work, we have, therefore, selected the latter as a mathematical model for modelling these intervals of a satellite image without fire, and then, we have determined the parameters of this Gaussian realization. So, when a fire occurs in this forest zone, we can use these parameters to calculate its belonging probability to the original image without fire. This probability should be very small because the fire, in any forest, can be considered as a rare event. As a consequence, we have presented a matrix image of the probability inverse of each interval for a better fire detection observation.Key words: belonging probability matrix image, forest fire, satellite image, thermal infra-red spectral band R esum e Nous pr esentons une m ethode de d etection pr ecoce des feux de forêts par image satellite en utilisant l'image matricielle de la probabilit e d'appartenance. Nous avons consid er e chaque ligne matricielle de l'image satellite comme une r ealisation d'un processus al eatoire non stationnaire dans la bande spectrale TIR (Thermal InfraRouge), puis divis e chaque ligne en tr es petits intervalles stationnaires et ergodiques, afin d'obtenir un mod ele math ematique ad equat. Ensuite, les pixels de l'image satellite sont consid er es comme statistiquement ind ependants, et donc chaque petit intervalle de chaque ligne se comporte, naturellement, comme un bruit gaussien stationnaire. Dans ce travail, nous avons donc s electionn e ce dernier comme mod ele math ematique pour mod eliser ces intervalles d'une image satellite sans feu et nous avons d etermin e les param etres de cette r ealisation gaussienne. Ainsi, lorsqu'un feu survient dans cette zone de forêt, nous pouvons utiliser ces param etres pour calculer sa probabilit e d'appartenance a l'image originale sans feu. Cette probabilit e doitêtre tr es faible puisque le feu, dans toute forêt, peutêtre consid er e comme un ev enement rare. Par cons equent, nous pr esentons une image matricielle de l'inverse de la probabilit e de chaque intervalle pour une meilleure observation de d etection des feux.
In this paper, we studied the contribution of the Algerian Alsat-1 satellite image and its effects on reducing false alarm rates when detecting or monitoring forest fires. We used the classical Support Vector Machines classification method which required positive and negative database training sets. Experiments demonstrate that, such Alsat-1 images, similar products of nearest characteristics satellites ensure very lower rates of false alarm rates without treating about detecting rates.
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