Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator

, ,
1 Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States
2 Department of Computer & Information Science & Engineering, University of Florida, Gainesville, Florida, United States
DOI
10.7287/peerj.preprints.27052v1
Published
Accepted
Subject Areas
Plant Science, Computational Science, Data Mining and Machine Learning, Data Science
Keywords
tree crown, classification, hyperspectral, multiple instance
Copyright
© 2018 Zou et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Zou S, Gader P, Zare A. 2018. Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator. PeerJ Preprints 6:e27052v1

Abstract

Tree species classification using hyperspectral imagery is a challenging task due to the high spectral similarity between species and large intra-species variability. This paper proposes a solution using the Multiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a discriminative target signature to differentiate between a pair of tree species while accounting for label uncertainty. Additionally, the performance of MI-ACE does not rely on parameter settings that require tuning resulting in a method that is easy to use in application. Results presented are using training and testing data provided by a data analysis competition aimed at encouraging the development of methods for extracting ecological information through remote sensing obtained through participation in the competition.

Author Comment

This is a submission to PeerJ for review.