期刊
PEERJ
卷 7, 期 -, 页码 -出版社
PEERJ INC
DOI: 10.7717/peerj.6101
关键词
Airborne remote sensing; Data alignment; Species classification; Crown segmentation; National ecological observatory network; Crown delineation; Remote sensing; Data science competition; Multilayer perceptron
资金
- NIST IAD Data Science Research Program
- Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative [GBMF4563]
- NSF Dimension of Biodiversity program [DEB-1442280]
- National Science Foundation
To accelerate scientific progress on remote tree classification-as well as biodiversity and ecology sampling-The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm classified species or genus with high accuracy (92.7% and 95.9%, respectively) on the training data and performed better than the other two algorithms (85.8-93.5%). This indicates promise for the use of the multilayer perceptron (MLP) algorithm for tree-species classification based on hyperspectral and LiDAR observations and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithm for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to classify species at the crown level. The average accuracy of these classifications on the test set was 68.8% for the nine species.
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