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Journal of Applied Remote Sensing • new

Deep learning for phenomena-based classification of Earth science images
Author(s): Manil Maskey; Rahul Ramachandran; Jeffrey Miller
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Paper 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.

Paper Details

Date Published: 1 September 2017
PDF: 11 pages
J. Appl. Remote Sens. 11(4) 042608 doi: 10.1117/1.JRS.11.042608
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Manil Maskey, NASA Marshall Space Flight Ctr. (United States)
The Univ. of Alabama in Huntsville (United States)
Rahul Ramachandran, NASA Marshall Space Flight Ctr. (United States)
Jeffrey Miller, The Univ. of Alabama in Huntsville (United States)


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