Hyperspectral Tree Crown Classification Using the Multiple Instance Adaptive Cosine Estimator

Published: July 26th, 2018

Category: Journal Papers

Abstract:

Tree species classification using hyperspectral imagery is a challenging task due to the high spectralsimilarity between species and large intra-species variability. This paper proposes a solution using theMultiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a discriminativetarget 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 resultingin a method that is easy to use in application. Results presented are using training and testing dataprovided by a data analysis competition aimed at encouraging the development of methods for extractingecological information through remote sensing obtained through participation in the competition.

Links:

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Citation:

Zou S, Gader P, Zare A. (2018) Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator. PeerJ Preprints 6:e27052v1 https://doi.org/10.7287/peerj.preprints.27052v1.
@Article{Zou2018Hyperspectral
Title = {Hyperspectral Tree Crown Classification Using the Multipleinstance Adaptive Cosine Estimator},
Author = {Zou, Z. and Gader, Paul and Zare, A. }
Journal = {PeerJ},
Year = {Under Review},
}