Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
为了提升变电站数据流检测的实时性与准确性,提出一种使用白鲸优化(beluga whale optimization,BWO)算法优化基于密度的噪声应用空间聚类(density based spatial clustering of applications with noise,DBSCAN)算法,与使用圆圈搜索算法...为了提升变电站数据流检测的实时性与准确性,提出一种使用白鲸优化(beluga whale optimization,BWO)算法优化基于密度的噪声应用空间聚类(density based spatial clustering of applications with noise,DBSCAN)算法,与使用圆圈搜索算法(circle search algorithm,CSA)优化单分类正则核极限学习机(one class regularized kernel extreme learning machine,OCRKELM)相结合的变电站通信网络数据流异常检测方法。首先,利用BWO-DBSCAN对正常数据流进行聚类,形成样本簇;其次,通过CSA-OCRKELM模型对异常数据流进行实时检测;最后,利用OPNET仿真软件仿真模拟变电站的通信行为并进行对比分析,验证所提方法的有效性。仿真实验结果表明所构建检测模型的检测率约为99%,较其他检测模型具有较高的性能与准确率。展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
文摘为了提升变电站数据流检测的实时性与准确性,提出一种使用白鲸优化(beluga whale optimization,BWO)算法优化基于密度的噪声应用空间聚类(density based spatial clustering of applications with noise,DBSCAN)算法,与使用圆圈搜索算法(circle search algorithm,CSA)优化单分类正则核极限学习机(one class regularized kernel extreme learning machine,OCRKELM)相结合的变电站通信网络数据流异常检测方法。首先,利用BWO-DBSCAN对正常数据流进行聚类,形成样本簇;其次,通过CSA-OCRKELM模型对异常数据流进行实时检测;最后,利用OPNET仿真软件仿真模拟变电站的通信行为并进行对比分析,验证所提方法的有效性。仿真实验结果表明所构建检测模型的检测率约为99%,较其他检测模型具有较高的性能与准确率。
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
