A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA.

Lotfi Tlig,Mounir Sayadi,Eric Moreau,Mohamed Tlig,Moez Bouchouicha

doi:10.3390/s20226429

Abstract

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

Highlights

The conventional detection systems of smoke and fire use sensors [1]
In addition to synthetic images, we shall evaluate the performance of our method on natural images
Be seen that different classesapproach are with an end to application in fire forest image

Summary

Introduction

The conventional detection systems of smoke and fire use sensors [1]. One of the major drawbacks, is that the systems do not issue the alarm unless the particles reach the sensors [2]. As an appropriate alternative to conventional techniques, vision-based fire and smoke detection methods have been adopted. Smoke and fire are regarded as a specific kind of texture. It is difficult to accurately detect the appearance of mentioned regions from images due to large variations of color intensities and texture. Many research works confirmed that texture features play a very important role in smoke and fire detection [3,4]. A wide recent work demonstrated that the multi-scale based techniques play an important role in smoke and texture classification [5,6]

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Results

Conclusion