基于无人机多光谱影像的亚热带阔叶林分类

Classification of Subtropical Broadleaf Forests Based on UAV Multispectral Imagery

使用无人机平台获取亚热带阔叶林样地的多光谱影像和可见光影像,并将多光谱影像与可见光影像进行波段合成获得添加多光谱信息的可见光组合影像,从可见光影像、多光谱影像和组合影像中分别提取光谱信息与空间信息,采用支持向量机、最大似然法、神经网络和马氏距离等4种分类算法对其优势树种进行分类。比较不同影像在不同分类算法下的分类结果。仅使用可见光影像使用不同算法分类的精度都比较低,最高精度为采用支持向量机的分类,分类精度为62.97%,Kappa系数为0.225 6。使用多光谱影像和添加多光谱影像的可见光组合影像分类精度均有提升。组合影像与支持向量机的分类组合达到90.64%,Kappa系数为0.786。使用低成本的无人机可见光影像分类精度较低,而添加多光谱影像能较大提升分类精度。

The UAV platform was used to acquire multispectral images and visible images of subtropical broad-leaved forest sample sites, and the multispectral images and visible images were band-synthesized to obtain a visible combination image with multispectral information.The dominant species were classified using four classification algorithms: support vector machine, maximum likelihood, neural network and Marxian distance.The classification results of different images were compared under different classification algorithms.The classification accuracies using only visible images in different algorithms were low, with the highest accuracy being the classification using support vector machines with 62.97% and a kappa coefficient of 0.225 6.The classification accuracies of the visible combined images using multispectral images and with the addition of multispectral images were improved.The combination of the combined image and the support vector machine achieved a classification accuracy of 90.64% with a Kappa coefficient of 0.786.The use of low-cost UAV visible images had a low classification accuracy, while the addition of multispectral images could improve the classification accuracy significantly.