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Unsupervised feature learning in remote sensing
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Paper Abstract

The need for labeled data is among the most common and well-known practical obstacles to deploying deep learning algorithms to solve real-world problems. The current generation of learning algorithms requires a large volume of data labeled according to a static and pre-defined schema. Conversely, humans can quickly learn generalizations based on large quantities of unlabeled data, and turn these generalizations into classifications using spontaneous labels, often including labels not seen before. We apply a state-of-the-art unsupervised learning algorithm to the noisy and extremely imbalanced xView data set to train a feature extractor that adapts to several tasks: visual similarity search that performs well on both common and rare classes; identifying outliers within a labeled data set; and learning a natural class hierarchy automatically.

Paper Details

Date Published: 6 September 2019
PDF: 13 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390H (6 September 2019); doi: 10.1117/12.2529791
Show Author Affiliations
Aaron Reite, NGA Research (United States)
Scott Kangas, Etegent Technologies, Ltd. (United States)
Zackery Steck, Etegent Technologies, Ltd. (United States)
Steven Goley, Etegent Technologies, Ltd. (United States)
Jonathan Von Stroh, CACI (United States)
Steven Forsyth, NVIDIA Corp. (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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