Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leav...Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leaves at different heights.We established a solid scatterer model to explain the widened durations found in analyzing the relationship between laser pulses and forest canopies,and obtained the corresponding rule between laser pulse duration and scatterer depth.Based on returned waveform characteristics,scatterers were classified into three types:simple,solid and complex.We developed single-peak derivative and multiple-peak derivative analysis methods to retrieve waveform features and discriminate between scatterer types.Solid scatterer simulations showed that the returned waveforms were widened as scatterer depth increased,and as space between sub-scatterers increased the returned waveforms developed two peaks which subsequently developed into two separate sub-waveforms.There were slight differences between the durations of simulated and measured waveforms.LIDAR waveform data are able to describe the backscatter characteristics of forest canopies,and have potential to improve the estimation accuracy of forest parameters.展开更多
We propose a new scheme for transformer differential protection. This scheme uses different characteristics of the differential currents waveforms (DCWs) under internal fault and magnetizing inrush current conditions....We propose a new scheme for transformer differential protection. This scheme uses different characteristics of the differential currents waveforms (DCWs) under internal fault and magnetizing inrush current conditions. The scheme is based on choosing an appropriate feature of the waveform and monitoring it during the post-disturbance instants. For this purpose, the signal feature is quantified by a discrimination function (DF). Discrimination between internal faults and magnetizing inrush currents is carried out by tracking the signs of three decision-making functions (DMFs) computed from the DFs for three phases. We also present a new algorithm related to the general scheme. The algorithm is based on monitoring the second derivative sign of DCW. The results show that all types of internal faults, even those accompanied by the magnetizing inrush, can be correctly identified from the inrush conditions about half a cycle after the occurrence of a disturbance. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. Furthermore, the proposed algorithm does not require burdensome computations.展开更多
A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud respectively.It aims to partially overcome the ineffectiveness of many traditional class...A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud respectively.It aims to partially overcome the ineffectiveness of many traditional classifiers caused by the fact that point cloud is lacking spectral information.The whole flowchart of the method is as follows:Firstly,Gaussian decomposition was applied to fit an echo full-waveform.The parameters associated with the Gaussian function were optimized by LM(Levenberg-Marquard)algorithm.Six and thirteen features were generated to describe the waveform characteristics and the local geometry of point cloud,respectively.Secondly,a random forest was selected as the classifier to which the generated features were input.Relief-F was used to rank the weights of all the features generated.Finally,features were input to the classifier one by one according to the weights calculated from feature ranking,where classification accuracies were evaluated.The experimental results show that the effectiveness of the fusion of features generated from waveform and point cloud for LiDAR data classification,with 95.4%overall accuracy,0.90 kappa coefficient,which outperform the results obtained by a single class of features,no matter whether they were generated from point cloud or waveform data.展开更多
基金supported by the National Basic Research Program of China(Grant No.2007CB714404)the Central PublicInterest Scientific Institution Basal Research Fund of China(Grant No.IFRIT200803)the National HiTech Research and Development Program of China(Grant No.2009AA12Z1461)
文摘Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leaves at different heights.We established a solid scatterer model to explain the widened durations found in analyzing the relationship between laser pulses and forest canopies,and obtained the corresponding rule between laser pulse duration and scatterer depth.Based on returned waveform characteristics,scatterers were classified into three types:simple,solid and complex.We developed single-peak derivative and multiple-peak derivative analysis methods to retrieve waveform features and discriminate between scatterer types.Solid scatterer simulations showed that the returned waveforms were widened as scatterer depth increased,and as space between sub-scatterers increased the returned waveforms developed two peaks which subsequently developed into two separate sub-waveforms.There were slight differences between the durations of simulated and measured waveforms.LIDAR waveform data are able to describe the backscatter characteristics of forest canopies,and have potential to improve the estimation accuracy of forest parameters.
文摘We propose a new scheme for transformer differential protection. This scheme uses different characteristics of the differential currents waveforms (DCWs) under internal fault and magnetizing inrush current conditions. The scheme is based on choosing an appropriate feature of the waveform and monitoring it during the post-disturbance instants. For this purpose, the signal feature is quantified by a discrimination function (DF). Discrimination between internal faults and magnetizing inrush currents is carried out by tracking the signs of three decision-making functions (DMFs) computed from the DFs for three phases. We also present a new algorithm related to the general scheme. The algorithm is based on monitoring the second derivative sign of DCW. The results show that all types of internal faults, even those accompanied by the magnetizing inrush, can be correctly identified from the inrush conditions about half a cycle after the occurrence of a disturbance. Another advantage of the proposed method is that the fault detection algorithm does not depend on the selection of thresholds. Furthermore, the proposed algorithm does not require burdensome computations.
基金National High Resolution Earth Observation Foundation(No.11-H37B02-9001-19/22)National Natural Science Foundation of China(No.41601504)National Key R&D Program of China(No.2018YFB0504500)。
文摘A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud respectively.It aims to partially overcome the ineffectiveness of many traditional classifiers caused by the fact that point cloud is lacking spectral information.The whole flowchart of the method is as follows:Firstly,Gaussian decomposition was applied to fit an echo full-waveform.The parameters associated with the Gaussian function were optimized by LM(Levenberg-Marquard)algorithm.Six and thirteen features were generated to describe the waveform characteristics and the local geometry of point cloud,respectively.Secondly,a random forest was selected as the classifier to which the generated features were input.Relief-F was used to rank the weights of all the features generated.Finally,features were input to the classifier one by one according to the weights calculated from feature ranking,where classification accuracies were evaluated.The experimental results show that the effectiveness of the fusion of features generated from waveform and point cloud for LiDAR data classification,with 95.4%overall accuracy,0.90 kappa coefficient,which outperform the results obtained by a single class of features,no matter whether they were generated from point cloud or waveform data.
