A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA
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 prote...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/22/6429 |
| _version_ | 1797548293168824320 |
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| author | Lotfi Tlig Moez Bouchouicha Mohamed Tlig Mounir Sayadi Eric Moreau |
| author_facet | Lotfi Tlig Moez Bouchouicha Mohamed Tlig Mounir Sayadi Eric Moreau |
| author_sort | Lotfi Tlig |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:57:17Z |
| format | Article |
| id | doaj.art-d575262fc9384b989d2eaefe845a817c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:57:17Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d575262fc9384b989d2eaefe845a817c2023-11-20T20:30:11ZengMDPI AGSensors1424-82202020-11-012022642910.3390/s20226429A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCALotfi Tlig0Moez Bouchouicha1Mohamed Tlig2Mounir Sayadi3Eric Moreau4Member of SIME Laboratory, ENSIT University of Tunis, Tunis 1008, TunisiaAix Marseille Univ, Université de Toulon, CNRS, LIS, 83041 Toulon, FranceMember of SIME Laboratory, ENSIT University of Tunis, Tunis 1008, TunisiaMember of SIME Laboratory, ENSIT University of Tunis, Tunis 1008, TunisiaAix Marseille Univ, Université de Toulon, CNRS, LIS, 83041 Toulon, FranceForests 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.https://www.mdpi.com/1424-8220/20/22/6429Gabor filteringPCA morphological transformationsfuzzy clusteringcolor image segmentationfire forest |
| spellingShingle | Lotfi Tlig Moez Bouchouicha Mohamed Tlig Mounir Sayadi Eric Moreau A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA Sensors Gabor filtering PCA morphological transformations fuzzy clustering color image segmentation fire forest |
| title | A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA |
| title_full | A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA |
| title_fullStr | A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA |
| title_full_unstemmed | A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA |
| title_short | A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA |
| title_sort | fast segmentation method for fire forest images based on multiscale transform and pca |
| topic | Gabor filtering PCA morphological transformations fuzzy clustering color image segmentation fire forest |
| url | https://www.mdpi.com/1424-8220/20/22/6429 |
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