Remote sensing tree classification with a multilayer perceptron

PeerJ

article

G Rex Sumsion¹  Michael S. Bradshaw¹  Kimball T. Hill¹  Lucas D.G. Pinto¹  Stephen R. Piccolo¹ 

[1] Department of Biology, Brigham Young University

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;   

DOI  :  10.7717/peerj.6101

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY