Pixel level smoke detection model with deep neural network Article Swipe
Muthukumaran Ramasubramanian
,
Aaron Kaulfus
,
Manil Maskey
,
Rahul Ramachandran
,
Iksha Gurung
,
Brian Freitag
,
Sundar Christopher
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1117/12.2532562
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1117/12.2532562
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/12.2532562
- OA Status
- green
- Cited By
- 7
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2980092754
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2980092754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1117/12.2532562Digital Object Identifier
- Title
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Pixel level smoke detection model with deep neural networkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-10-07Full publication date if available
- Authors
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Muthukumaran Ramasubramanian, Aaron Kaulfus, Manil Maskey, Rahul Ramachandran, Iksha Gurung, Brian Freitag, Sundar ChristopherList of authors in order
- Landing page
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https://doi.org/10.1117/12.2532562Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://ntrs.nasa.gov/api/citations/20190032194/downloads/20190032194.pdfDirect OA link when available
- Concepts
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Smoke, Geostationary orbit, Computer science, Convolutional neural network, Remote sensing, Multispectral image, Deep learning, Satellite imagery, Satellite, Pixel, Artificial neural network, Artificial intelligence, Environmental science, Meteorology, Geology, Engineering, Geography, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2024: 2, 2022: 2, 2021: 2, 2020: 1Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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