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Article

Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery

1
School of Technology, Beijing Forestry University, Beijing 100083, China
2
China Fire and Rescue Institute, Beijing 102202, China
3
Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry, Sault Ste Marie, ON P6A 5X6, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to the work.
Academic Editors: Fahimeh Farahnakian, Jukka Heikkonen and Pouya Jafarzadeh
Remote Sens. 2022, 14(1), 45; https://doi.org/10.3390/rs14010045
Received: 4 November 2021 / Revised: 16 December 2021 / Accepted: 20 December 2021 / Published: 23 December 2021
(This article belongs to the Special Issue Deep Learning and Computer Vision in Remote Sensing)
Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke. View Full-Text
Keywords: forest fire; remote sensing; smoke segmentation; Smoke-Unet; attention mechanism; residual block; Landsat-8; band sensibility forest fire; remote sensing; smoke segmentation; Smoke-Unet; attention mechanism; residual block; Landsat-8; band sensibility
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MDPI and ACS Style

Wang, Z.; Yang, P.; Liang, H.; Zheng, C.; Yin, J.; Tian, Y.; Cui, W. Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sens. 2022, 14, 45. https://doi.org/10.3390/rs14010045

AMA Style

Wang Z, Yang P, Liang H, Zheng C, Yin J, Tian Y, Cui W. Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sensing. 2022; 14(1):45. https://doi.org/10.3390/rs14010045

Chicago/Turabian Style

Wang, Zewei, Pengfei Yang, Haotian Liang, Change Zheng, Jiyan Yin, Ye Tian, and Wenbin Cui. 2022. "Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery" Remote Sensing 14, no. 1: 45. https://doi.org/10.3390/rs14010045

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