ABSTRACT

Urban tree species classification is a challenging task due to spectral and spatial diversity within an urban environment. Unmanned aerial vehicle (UAV) platforms and small-sensor technology are rapidly evolving, presenting the opportunity for a comprehensive multi-sensor remote sensing approach for urban tree classification. The objectives of this paper were to develop a multi-sensor data fusion technique for urban tree species classification with limited training samples. To that end, UAV-based multispectral, hyperspectral, LiDAR, and thermal infrared imagery was collected over an urban study area to test the classification of 96 individual trees from seven species using a data fusion approach. Two supervised machine learning classifiers, Random Forest (RF) and Support Vector Machine (SVM), were investigated for their capacity to incorporate highly dimensional and diverse datasets from multiple sensors. When using hyperspectral-derived spectral features with RF, the fusion of all features extracted from all sensor types (spectral, LiDAR, thermal) achieved the highest overall classification accuracy (OA) of 83.3% and kappa of 0.80. Despite multispectral reflectance bands alone producing significantly lower OA of 55.2% compared to 70.2% with minimum noise fraction (MNF) transformed hyperspectral reflectance bands, the full dataset combination (spectral, LiDAR, thermal) with multispectral-derived spectral features achieved an OA of 81.3% and kappa of 0.77 using RF. Comparison of the features extracted from individual sensors for each species highlight the ability for each sensor to identify distinguishable characteristics between species to aid classification. The results demonstrate the potential for a high-resolution multi-sensor data fusion approach for classifying individual trees by species in a complex urban environment under limited sampling requirements.

摘要

由于城市环境中的光谱和空间多样性，城市树种分类是一项具有挑战性的任务。无人机 (UAV) 平台和小型传感器技术正在迅速发展，为城市树木分类的综合多传感器遥感方法提供了机会。本文的目的是开发一种多传感器数据融合技术，用于在训练样本有限的情况下进行城市树种分类。为此，在城市研究区域收集了基于无人机的多光谱、高光谱、LiDAR 和热红外图像，以使用数据融合方法测试来自 7 个物种的 96 棵树木的分类。两个监督机器学习分类器，随机森林 (RF) 和支持向量机 (SVM)，研究了它们整合来自多个传感器的高维度和多样化数据集的能力。将高光谱衍生光谱特征与 RF 结合使用时，从所有传感器类型（光谱、激光雷达、热传感器）中提取的所有特征的融合实现了 83.3% 的最高总体分类精度 (OA) 和 0.80 的 kappa。尽管与最小噪声分数 (MNF) 转换的高光谱反射带的 70.2% 相比，单独的多光谱反射带产生显着较低的 55.2% 的 OA，但具有多光谱衍生光谱特征的完整数据集组合（光谱、激光雷达、热）实现了 81.3 的 OA % 和 kappa 为 0.77，使用 RF。比较从每个物种的单个传感器提取的特征，突出了每个传感器识别物种之间可区分特征以帮助分类的能力。结果证明了高分辨率多传感器数据融合方法的潜力，可在有限的采样要求下，在复杂的城市环境中按物种对个别树木进行分类。