1 September 2017 Deep learning for phenomena-based classification of Earth science images
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J. of Applied Remote Sensing, 11(4), 042608 (2017). doi:10.1117/1.JRS.11.042608
Abstract
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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Manil Maskey, Rahul Ramachandran, Jeffrey Miller, "Deep learning for phenomena-based classification of Earth science images," Journal of Applied Remote Sensing 11(4), 042608 (1 September 2017). http://dx.doi.org/10.1117/1.JRS.11.042608 Submission: Received 30 April 2017; Accepted 3 August 2017
Submission: Received 30 April 2017; Accepted 3 August 2017
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KEYWORDS
Earth sciences

Image classification

Convolution

Air contamination

Satellites

MODIS

Data modeling

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