Zou Sheng, Gader Paul, Zare Alina
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America.
Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, United States of America.
PeerJ. 2019 Feb 28;7:e6405. doi: 10.7717/peerj.6405. eCollection 2019.
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. Multi-class species classification is achieved by training a set of one-vs-one MI-ACE classifiers corresponding to the classification between each pair of tree species and a majority voting on the classification results from all classifiers. 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. The experimental results using one-vs-one MI-ACE technique composed of a hierarchical classification, where a tree crown is first classified to one of the genus classes and one of the species classes. The species-level rank-1 classification accuracy is 86.4% and cross entropy is 0.9395 on the testing data, provided by the competition organizer, without the release of ground truth for testing data. Similarly, the same evaluation metrics are computed on the training data, where the rank-1 classification accuracy is 95.62% and the cross entropy is 0.2649. The results show that the presented approach can not only classify the majority species classes, but also classify the rare species classes.
由于树种之间光谱相似度高且种内变异性大，利用高光谱图像进行树种分类是一项具有挑战性的任务。本文提出了一种使用多实例自适应余弦估计器（MI-ACE）算法的解决方案。MI-ACE估计一个判别性目标特征，以区分一对树种，同时考虑标签不确定性。通过训练一组对应于每对树种之间分类的一对一MI-ACE分类器，并对所有分类器的分类结果进行多数投票，实现多类树种分类。此外，MI-ACE的性能不依赖于需要调整的参数设置，从而形成一种易于在应用中使用的方法。所呈现的结果使用了由一场数据分析竞赛提供的训练和测试数据，该竞赛旨在鼓励开发通过参与竞赛获得的遥感数据提取生态信息的方法。使用由层次分类组成的一对一MI-ACE技术的实验结果表明，首先将树冠分类到属类之一和物种类之一。在竞赛组织者提供的测试数据上，物种级别的一级分类准确率为86.4%，交叉熵为0.9395，且未公布测试数据的地面真值。同样，在训练数据上计算相同的评估指标，一级分类准确率为95.62%，交叉熵为0.2649。结果表明，所提出的方法不仅可以对大多数物种类进行分类，还可以对稀有物种类进行分类。
