SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 14; p. 1702
• Main Authors: Ba, Rui, Chen, Chen, Yuan, Jing, Song, Weiguo, Lo, Siuming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.07.2019
• Subjects: Artificial neural networks; attention; Cameras; Classification; CNN; Coefficients; Convolution; Datasets; Deep learning; dust; environmental assessment; Forest fire detection; Haze; Identification; Image classification; Image detection; Machine learning; Methods; Model accuracy; moderate resolution imaging spectroradiometer; monitoring; Neural networks; Remote sensing; Satellite imagery; Satellites; scene classification; Sensors; Smoke; smoke detection; Spectroradiometers; State-of-the-art reviews; Surveillance; Training; wildfire; Wildfires; United States > US; China

A variety of environmental analysis applications have been advanced by the use of satellite remote sensing. Smoke detection based on satellite imagery is imperative for wildfire detection and monitoring. However, the commonly used smoke detection methods mainly focus on smoke discrimination from a f...