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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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.