Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this...The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.展开更多
Fault diagnosis techniques,which are crucial in the field of industrial intelligent manufacturing,are capable of equipment performance maintenance and productivity improvement.In fault diagnosis,multi-type sensors are...Fault diagnosis techniques,which are crucial in the field of industrial intelligent manufacturing,are capable of equipment performance maintenance and productivity improvement.In fault diagnosis,multi-type sensors are commonly used for monitoring because a single data source fails to provide sufficient information to support the comprehensive analysis and accurate diagnosis.Hidden information between modes can be mined using data fusion techniques,enabling more effective decision-making and condition analysis.However,the data measured by multiple sensors are subject to issues such as varying types,an imbalanced ratio of positive to negative samples,and significant differences in data structure,making multi-source data fusion and inter-feature information acquisition challenging.To address these problems,we propose a fault diagnosis method based on dynamic convolution and polarized self-attention(DC-PSA)feature fusion networks.Given that unimodal features are not utilized comprehensively enough,we propose a dynamic convolution-based feature self-convergence model.The ability of the model is improved by attentively aggregating multiple convolution kernels,which are combined in a form dynamically adjusted according to different inputs to fully utilize the features.To enable effective feature-level integration across modalities,we establish a cross-attention-based multimodal fusion model,where each modal branch learns multiscale spatial information independently and forms cross-channel interactions in a localized manner,which can realize the information interactions between local and global channel attention.Empirical results on the Paderborn benchmark dataset validate that the proposed method captures the complementary characteristics across signal types more effectively than existing methods,leading to a notable boost in diagnostic accuracy following the fusion process.The accuracy of the proposed model reached 98.6%,representing an improvement of 8.74%compared to the baseline model.展开更多
The leaf area index(LAI)is crucial for precision agriculture management.UAV remote sensing technology has been widely applied for LAI estimation.Although spectral features are widely used for LAI estimation,their perf...The leaf area index(LAI)is crucial for precision agriculture management.UAV remote sensing technology has been widely applied for LAI estimation.Although spectral features are widely used for LAI estimation,their performance is often constrained in complex agricultural scenarios due to interference from soil background reflectance,variations in lighting conditions,and vegetation heterogeneity.Therefore,this study evaluates the potential of multi-source feature fusion and convolutional neural networks(CNN)in estimating maize LAI.To achieve this goal,field experiments on maize were conducted in Xinxiang City and Xuzhou City,China.Subsequently,spectral features,texture features,and crop height were extracted from the multi-spectral remote sensing data to construct a multi-source feature dataset.Then,maize LAI estimation models were developed using multiple linear regression,gradient boosting decision tree,and CNN.The results showed that:(1)Multi-source feature fusion,which integrates spectral features,texture features,and crop height,demonstrated the highest accuracy in LAI estimation,with the R^(2) ranging from 0.70 to 0.83,the RMSE ranging from 0.44 to 0.60,and the rRMSE ranging from 10.79%to 14.57%.In addition,the multi-source feature fusion demonstrates strong adaptability across different growth environments.In Xinxiang,the R^(2) ranges from 0.76 to 0.88,the RMSE ranges from 0.35 to 0.50,and the rRMSE ranges from 8.73%to 12.40%.In Xuzhou,the R^(2) ranges from 0.60 to 0.83,the RMSE ranges from 0.46 to 0.71,and the rRMSE ranges from 10.96%to 17.11%.(2)The CNN model outperformed traditional machine learning algorithms in most cases.Moreover,the combination of spectral features,texture features,and crop height using the CNN model achieved the highest accuracy in LAI estimation,with the R^(2) ranging from 0.83 to 0.88,the RMSE ranging from 0.35 to 0.46,and the rRMSE ranging from 8.73%to 10.96%.展开更多
Trajectory prediction(TP)is critical for enhancing flight safety and operational reliability in small to medium-sized private and corporate aircraft,which involve complex multiple inputs and multiple outputs.While exi...Trajectory prediction(TP)is critical for enhancing flight safety and operational reliability in small to medium-sized private and corporate aircraft,which involve complex multiple inputs and multiple outputs.While existing TP methods primarily focus on extracting coupled features,they often neglect the independent features of individual outputs,leading to unsatisfactory learning performance.To address this limitation,this paper proposes a multitask learning-based TP method using bi-directional long short-term memory(Bi-LSTM),consisting of two key components:1)a multi-source feature fusion part that automatically extracts and integrates coupled evolutionary features across flight modes,and 2)a multitask learning part that mines independent change characteristics of each output.Firstly,the trajectory sequences are categorized into short,medium,and long-period flight modes to better capture temporal dependencies.The coupled characteristics in every flight mode are automatically excavated and integrated via the Bi-LSTM and fully connected network in the multi-source feature fusion part.Secondly,the fusion output is sent into the multitask learning part and every task has a customized model to learn independent evolutionary features of each output.Experimental results on real-world flight trajectories demonstrate the superiority of the proposed method in both one-step and multi-step prediction scenarios,highlighting its ability to leverage both coupled and independent features of flight trajectories.展开更多
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
基金supported by the National Key Research and De-velopment Program of China(No.2020YFB0505601).
文摘The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3307300)the National Natural Science Foundation of China(Grant Nos.62125301,62021003,62373014,92467205)+1 种基金the Beijing Nova Program(Grant No.20240484694)the Beijing Youth Scholar(Grant No.037)。
文摘Fault diagnosis techniques,which are crucial in the field of industrial intelligent manufacturing,are capable of equipment performance maintenance and productivity improvement.In fault diagnosis,multi-type sensors are commonly used for monitoring because a single data source fails to provide sufficient information to support the comprehensive analysis and accurate diagnosis.Hidden information between modes can be mined using data fusion techniques,enabling more effective decision-making and condition analysis.However,the data measured by multiple sensors are subject to issues such as varying types,an imbalanced ratio of positive to negative samples,and significant differences in data structure,making multi-source data fusion and inter-feature information acquisition challenging.To address these problems,we propose a fault diagnosis method based on dynamic convolution and polarized self-attention(DC-PSA)feature fusion networks.Given that unimodal features are not utilized comprehensively enough,we propose a dynamic convolution-based feature self-convergence model.The ability of the model is improved by attentively aggregating multiple convolution kernels,which are combined in a form dynamically adjusted according to different inputs to fully utilize the features.To enable effective feature-level integration across modalities,we establish a cross-attention-based multimodal fusion model,where each modal branch learns multiscale spatial information independently and forms cross-channel interactions in a localized manner,which can realize the information interactions between local and global channel attention.Empirical results on the Paderborn benchmark dataset validate that the proposed method captures the complementary characteristics across signal types more effectively than existing methods,leading to a notable boost in diagnostic accuracy following the fusion process.The accuracy of the proposed model reached 98.6%,representing an improvement of 8.74%compared to the baseline model.
基金funded by the National Key R&D Program of China(2023YFD1900705)the Central Public-interest Scientific Institution Basal Research Fund(No.IFI2024-01).
文摘The leaf area index(LAI)is crucial for precision agriculture management.UAV remote sensing technology has been widely applied for LAI estimation.Although spectral features are widely used for LAI estimation,their performance is often constrained in complex agricultural scenarios due to interference from soil background reflectance,variations in lighting conditions,and vegetation heterogeneity.Therefore,this study evaluates the potential of multi-source feature fusion and convolutional neural networks(CNN)in estimating maize LAI.To achieve this goal,field experiments on maize were conducted in Xinxiang City and Xuzhou City,China.Subsequently,spectral features,texture features,and crop height were extracted from the multi-spectral remote sensing data to construct a multi-source feature dataset.Then,maize LAI estimation models were developed using multiple linear regression,gradient boosting decision tree,and CNN.The results showed that:(1)Multi-source feature fusion,which integrates spectral features,texture features,and crop height,demonstrated the highest accuracy in LAI estimation,with the R^(2) ranging from 0.70 to 0.83,the RMSE ranging from 0.44 to 0.60,and the rRMSE ranging from 10.79%to 14.57%.In addition,the multi-source feature fusion demonstrates strong adaptability across different growth environments.In Xinxiang,the R^(2) ranges from 0.76 to 0.88,the RMSE ranges from 0.35 to 0.50,and the rRMSE ranges from 8.73%to 12.40%.In Xuzhou,the R^(2) ranges from 0.60 to 0.83,the RMSE ranges from 0.46 to 0.71,and the rRMSE ranges from 10.96%to 17.11%.(2)The CNN model outperformed traditional machine learning algorithms in most cases.Moreover,the combination of spectral features,texture features,and crop height using the CNN model achieved the highest accuracy in LAI estimation,with the R^(2) ranging from 0.83 to 0.88,the RMSE ranging from 0.35 to 0.46,and the rRMSE ranging from 8.73%to 10.96%.
文摘Trajectory prediction(TP)is critical for enhancing flight safety and operational reliability in small to medium-sized private and corporate aircraft,which involve complex multiple inputs and multiple outputs.While existing TP methods primarily focus on extracting coupled features,they often neglect the independent features of individual outputs,leading to unsatisfactory learning performance.To address this limitation,this paper proposes a multitask learning-based TP method using bi-directional long short-term memory(Bi-LSTM),consisting of two key components:1)a multi-source feature fusion part that automatically extracts and integrates coupled evolutionary features across flight modes,and 2)a multitask learning part that mines independent change characteristics of each output.Firstly,the trajectory sequences are categorized into short,medium,and long-period flight modes to better capture temporal dependencies.The coupled characteristics in every flight mode are automatically excavated and integrated via the Bi-LSTM and fully connected network in the multi-source feature fusion part.Secondly,the fusion output is sent into the multitask learning part and every task has a customized model to learn independent evolutionary features of each output.Experimental results on real-world flight trajectories demonstrate the superiority of the proposed method in both one-step and multi-step prediction scenarios,highlighting its ability to leverage both coupled and independent features of flight trajectories.
