Artificial Intelligence-Based Detection of Smoke Plume

被引：0

作者：

Jeong, Yemin ^{[1
]}

Youn, Youjeong ^{[1
]}

Kim, Seoyeon ^{[1
]}

Kang, Jonggu ^{[1
]}

Choi, Soyeon ^{[1
]}

Im, Yungyo ^{[1
]}

Seo, Youngmin ^{[1
]}

Yu, Jeong-Ah ^{[2
]}

Sung, Kyoung-Hee ^{[2
]}

Kim, Sang-Min ^{[2
]}

Lee, Yangwon ^{[1
]}

机构：

[1] Pukyong Natl Univ, Dept Spatial Informat Engn, Div Earth Environm Syst Sci, Busan, South Korea

[2] Natl Inst Environm Res, Environm Satellite Ctr, Incheon, South Korea

来源：

KOREAN JOURNAL OF REMOTE SENSING | 2023年 / 39卷 / 02期

关键词：

GEMS; Artificial intelligence; Smoke plume; Yellow dust; False color composite; ACTIVE FIRE DETECTION; WILDFIRE; IMAGERY; IMPACT;

D O I：

10.7780/kjrs.2023.39.5.2.10

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

Wildfires cause a lot of environmental and economic damage to the Earth over time. Various experiments have examined the harmful effects of wildfires. Also, studies for detecting wildfires and pollutant emissions using satellite remote sensing have been conducted for many years. The wildfire product for the Geostationary Environmental Monitoring Spectrometer (GEMS), Korea's first environmental satellite sensor, has not been provided yet. In this study, a false-color composite for better expression of wildfire smoke was created from GEMS and used in a U-Net model for wildfire detection. Then, a classification model was constructed to distinguish yellow dust from the wildfire smoke candidate pixels. The proposed method can contribute to disaster monitoring using GEMS images.

引用

页码：859 / 873

页数：15

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