Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator
- 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
- 2018. Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator. PeerJ Preprints 6:e27052v1 https://doi.org/10.7287/peerj.preprints.27052v1
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.