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

Sensors

Mounir Sayadi¹  Moez Bouchouicha¹  Eric Moreau¹  Lotfi Tlig²  Mohamed Tlig² 

[1] Aix Marseille Univ, Université de Toulon, CNRS, LIS, 83041 Toulon, France;Member of SIME Laboratory, ENSIT University of Tunis, Tunis 1008, Tunisia;

关键词: Gabor filtering;    PCA morphological transformations;    fuzzy clustering;    color image segmentation;    fire forest;   

DOI  :  10.3390/s20226429

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown