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Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator

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RT @Gator_Sense: Preprint describing entry into this competition using @NEON_sci data is now posted on @PeerJPreprints https://t.co/kcHWHT9…
RT @Gator_Sense: Preprint describing entry into this competition using @NEON_sci data is now posted on @PeerJPreprints https://t.co/kcHWHT9…
447 days ago
RT @Gator_Sense: Preprint describing entry into this competition using @NEON_sci data is now posted on @PeerJPreprints https://t.co/kcHWHT9…
447 days ago
Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator https://t.co/qioYwe4MWS
RT @Gator_Sense: Preprint describing entry into this competition using @NEON_sci data is now posted on @PeerJPreprints https://t.co/kcHWHT9…
Preprint describing entry into this competition using @NEON_sci data is now posted on @PeerJPreprints https://t.co/kcHWHT9uez! Code on our @github site in @MATLAB and #Python implementations: https://t.co/oxNPodql2l Thanks for the great competition cant wait for next round! https://t.co/BIt4v8AvKI
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Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Sheng Zou conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper.

Paul Gader conceived and designed the experiments, analyzed the data.

Alina Zare conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, approved the final draft.

Data Deposition

The following information was supplied regarding data availability:

1) Hyperspectral Toolkit Code

2) N/A

3) https://zenodo.org/record/1260272

Funding

This material is based upon work supported by the National Science Foundation under Grant IIS-1723891-CAREER: Supervised Learning for Incomplete and Uncertain Data. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program. The ECODSE competition was supported, in part, by a research grant from NIST IAD Data Science Research Program to D.Z. Wang, E.P. White, and S. Bohlman, by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and by an NSF Dimension of Biodiversity program grant (DEB-1442280) to S. Bohlman. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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