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
In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This...In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
针对轧辊偏心信号是混杂在各种随机干扰中含有多次谐波的复杂高频周期信号,以及FFT(Fast Fourier Transform)法对轧辊偏心信号分析的局限性,研究了一种基于四阶累积量的Root-MUSIC(Multiple Signal Classification)法和Prony法相结合的...针对轧辊偏心信号是混杂在各种随机干扰中含有多次谐波的复杂高频周期信号,以及FFT(Fast Fourier Transform)法对轧辊偏心信号分析的局限性,研究了一种基于四阶累积量的Root-MUSIC(Multiple Signal Classification)法和Prony法相结合的轧辊偏心信号估计新方法.利用基于四阶累积量的Root-MUSIC法准确估计出偏心谐波的频率及谐波的个数,同时由Root-MUSIC求得的根直接使用Prony方法估计出偏心信号的各次谐波幅值和相位.仿真结果和实验结果也验证了结合方法的可行性和有效性,在信噪比较低的情况下仍具有较高的频谱分辨率和估计精度,能准确地同时估计出偏心谐波的频率、幅值及相位,尤其在频率分辨率和抗噪声上具有FFT法无法比拟的优越性.展开更多
针对轧辊偏心信号是混杂在各种随机干扰中的复杂高频周期信号,因FFT法对信号分析的局限性,提出了一种Root-MUSIC(Multiple Signal Classification)法和Prony法相结合的轧辊偏心信号估计新方法。利用Root-MUSIC法准确估计出偏心谐波的频...针对轧辊偏心信号是混杂在各种随机干扰中的复杂高频周期信号,因FFT法对信号分析的局限性,提出了一种Root-MUSIC(Multiple Signal Classification)法和Prony法相结合的轧辊偏心信号估计新方法。利用Root-MUSIC法准确估计出偏心谐波的频率及谐波的个数,同时使用Prony方法估计出偏心信号的各次谐波幅值和相位。仿真结果验证了可行性和有效性,在信噪比较低的情况下仍能准确地同时估计出偏心谐波的频率、幅值及相位,尤其在频率分辨率和抗噪声上比FFT法具有优越性。展开更多
To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of...To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.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.
基金Supported by the National Pre-research Program during the 14th Five-Year Plan(514010405)。
文摘In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
文摘针对轧辊偏心信号是混杂在各种随机干扰中含有多次谐波的复杂高频周期信号,以及FFT(Fast Fourier Transform)法对轧辊偏心信号分析的局限性,研究了一种基于四阶累积量的Root-MUSIC(Multiple Signal Classification)法和Prony法相结合的轧辊偏心信号估计新方法.利用基于四阶累积量的Root-MUSIC法准确估计出偏心谐波的频率及谐波的个数,同时由Root-MUSIC求得的根直接使用Prony方法估计出偏心信号的各次谐波幅值和相位.仿真结果和实验结果也验证了结合方法的可行性和有效性,在信噪比较低的情况下仍具有较高的频谱分辨率和估计精度,能准确地同时估计出偏心谐波的频率、幅值及相位,尤其在频率分辨率和抗噪声上具有FFT法无法比拟的优越性.
基金Scientific and technological development plan project in Jilin Province(20150519023JH)~~
文摘针对轧辊偏心信号是混杂在各种随机干扰中的复杂高频周期信号,因FFT法对信号分析的局限性,提出了一种Root-MUSIC(Multiple Signal Classification)法和Prony法相结合的轧辊偏心信号估计新方法。利用Root-MUSIC法准确估计出偏心谐波的频率及谐波的个数,同时使用Prony方法估计出偏心信号的各次谐波幅值和相位。仿真结果验证了可行性和有效性,在信噪比较低的情况下仍能准确地同时估计出偏心谐波的频率、幅值及相位,尤其在频率分辨率和抗噪声上比FFT法具有优越性。
基金supported by the National Natural Science Foundation of China (60604021 60874054)
文摘To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.
