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Author(s):
Zewei Wang, Pengfei Yang, Haotian Liang, Change Zheng, Jiyan Yin, Ye Tian, Wenbin Cui
Year Published:

Cataloging Information

Topic(s):
Mapping
Smoke & Air Quality
Fire & Smoke Models
Smoke Emissions
Smoke Emissions and Inventory

NRFSN number: 24252
FRAMES RCS number: 65120
Record updated:

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.

Citation

Wang, Zewei; Yang, Pengfei; Liang, Haotian; Zheng, Change; Yin, Jiyan; Tian, Ye; Cui, Wenbin. 2022. Semantic segmentation and analysis on sensitive parameters of forest fire smoke using Smoke-Unet and Landsat-8 imagery. Remote Sensing 14(1):45. https://doi.org/10.3390/rs14010045

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