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.
Automated classification of images across image archives requires reducing the semantic gap between high-level features perceived by humans and low-level features encoded in images. Due to rapidly growing image archives in the Earth science domain, it is critical to automatically classify images for efficient sorting and discovery. In particular, classifying images based on the presence of Earth science phenomena allows users to perform climatology studies and investigate case studies. We present applications of deep learning-based classification of Earth science images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.