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Pixel level smoke detection model with deep neural network
Author(s): Muthukumaran Ramasubramanian; Aaron Kaulfus; Manil Maskey; Rahul Ramachandran; Iksha Gurung; Brian Freitag; Sundar Christopher
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Paper Abstract

Traditional analysis of smoke extent from satellite imagery relies largely on spectral analysis using multispectral data thereby requiring large data volumes or subjective and time-consuming evaluation. These methods are not scalable to observing capabilities of the new generation of remote sensing platforms. We propose an automated, deep learning based detection model capable of identifying smoke plumes from shortwave reflectance for the Geostationary Operational Environmental Satellite R series of satellites. Hand-labelled, past instances of smoke plumes from the NOAA Hazard Mapping System, quality controlled for spatiotemporal accuracy by a subject matter expert, comprises the reference truth dataset. The detection pipeline comprises of pre-process, detection, and post-process stages. A Convolutional Neural Network (CNN), trained on smoke events with varying optical thicknesses and sun-satellite viewing geometry is used to predict the probability score for a given pixel containing smoke. The model is able to detect smoke over both low and high reflectance surfaces and discriminate smoke from clouds though challenges remain in identifying optically thin smoke. Finally, we discuss a web-based interface to visualize daily smoke prediction and analyze the predictions over time.

Paper Details

Date Published: 7 October 2019
PDF: 11 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115515 (7 October 2019); doi: 10.1117/12.2532562
Show Author Affiliations
Muthukumaran Ramasubramanian, The Univ. of Alabama in Huntsville (United States)
Aaron Kaulfus, The Univ. of Alabama in Huntsville (United States)
Manil Maskey, NASA Marshall Space Flight Ctr. (United States)
Rahul Ramachandran, NASA Marshall Space Flight Ctr. (United States)
Iksha Gurung, The Univ. of Alabama in Huntsville (United States)
Brian Freitag, The Univ. of Alabama in Huntsville (United States)
Sundar Christopher, The Univ. of Alabama in Huntsville (United States)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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