Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and uns...Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and unsupervised classification are often used to classify the remote sensing image.But they only use pixel brightness characteristics to classify it.So the classification accuracy is low and can not meet the needs of practical application.Decision tree classification is a new technology for remote sensing image classification.In this study,we select the rocky desertification areas Kaizuo Township as a case study,use the ASTER image data,DEM and lithology data,by extracting the normalized difference vegetation index,ratio vegetation index,terrain slope and other data to establish classification rules to build decision trees.In the ENVI software support,we access the classification images.By calculating the classification accuracy and kappa coefficient,we find that better classification results can be obtained,desertification information can be extracted automatically and if more remote sensing image bands used,higher resolution DEM employed and less errors data reduced during processing,classification accuracy can be improve further.展开更多
If the constraint boundary relates to a bifurcation parameter, a bifurcation is said to be parametrically constrained. Relying upon some substitution, a parametrically constrained bifurcation is transformed to an unco...If the constraint boundary relates to a bifurcation parameter, a bifurcation is said to be parametrically constrained. Relying upon some substitution, a parametrically constrained bifurcation is transformed to an unconstrained bifurcation about new variables. A general form of transition sets of the parametrically constrained bifurcation is derived. The result indicates that only the constrained bifurcation set is influenced by parametric constraints, while other transition sets are the same as those of the corresponding nonparametrically constrained bifurcation. Taking parametrically constrained pitchfork bifurcation problems as examples, effects of parametric constraints on bifurcation classification are discussed.展开更多
Neyman-Pearson classification has been studied in several articles before. But they all proceeded in the classes of indicator functions with indicator function as the loss function, which make the calculation to be di...Neyman-Pearson classification has been studied in several articles before. But they all proceeded in the classes of indicator functions with indicator function as the loss function, which make the calculation to be difficult. This paper investigates Neyman- Pearson classification with convex loss function in the arbitrary class of real measurable functions. A general condition is given under which Neyman-Pearson classification with convex loss function has the same classifier as that with indicator loss function. We give analysis to NP-ERM with convex loss function and prove it＇s performance guarantees. An example of complexity penalty pair about convex loss function risk in terms of Rademacher averages is studied, which produces a tight PAC bound of the NP-ERM with convex loss function.展开更多
The detection of partial discharge signals and classifying its patterns is an area of interest in the analysis of defects in high voltage cables. This paper investigates a filter-bank based approach to extract frequen...The detection of partial discharge signals and classifying its patterns is an area of interest in the analysis of defects in high voltage cables. This paper investigates a filter-bank based approach to extract frequency domain based features to represent partial discharge signals. By applying the fast Fourier transform, the sampled partial discharge data are mapped into equivalent discrete frequency bins, which are then grouped into N equal sub-bands and also octave sub-bands, each providing N- dimensional features for partial discharge pattern classifi- cation. Two classifiers, namely, the support vector machine and the sparse representation classifier, are implemented and their outputs are fused, in order to improve the accuracy of classifying partial discharge. Classification accuracy is also compared with wavelet domain based octave frequency sub-band features.展开更多
文摘Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and unsupervised classification are often used to classify the remote sensing image.But they only use pixel brightness characteristics to classify it.So the classification accuracy is low and can not meet the needs of practical application.Decision tree classification is a new technology for remote sensing image classification.In this study,we select the rocky desertification areas Kaizuo Township as a case study,use the ASTER image data,DEM and lithology data,by extracting the normalized difference vegetation index,ratio vegetation index,terrain slope and other data to establish classification rules to build decision trees.In the ENVI software support,we access the classification images.By calculating the classification accuracy and kappa coefficient,we find that better classification results can be obtained,desertification information can be extracted automatically and if more remote sensing image bands used,higher resolution DEM employed and less errors data reduced during processing,classification accuracy can be improve further.
基金Project supported by the National Natural Science Foundation of China (Nos. 10872142 and10632040)the New Century Excellent Talents Plan of the Ministry of Education of China(No. NCET-05-0247)the Key Project of Tianjin (No. 09JCZDJC26800)
文摘If the constraint boundary relates to a bifurcation parameter, a bifurcation is said to be parametrically constrained. Relying upon some substitution, a parametrically constrained bifurcation is transformed to an unconstrained bifurcation about new variables. A general form of transition sets of the parametrically constrained bifurcation is derived. The result indicates that only the constrained bifurcation set is influenced by parametric constraints, while other transition sets are the same as those of the corresponding nonparametrically constrained bifurcation. Taking parametrically constrained pitchfork bifurcation problems as examples, effects of parametric constraints on bifurcation classification are discussed.
基金This is a Plenary Report on the International Symposium on Approximation Theory and Remote SensingApplications held in Kunming, China in April 2006Supported in part by NSF of China under grants 10571010 , 10171007 and Startup Grant for Doctoral Researchof Beijing University of Technology
文摘Neyman-Pearson classification has been studied in several articles before. But they all proceeded in the classes of indicator functions with indicator function as the loss function, which make the calculation to be difficult. This paper investigates Neyman- Pearson classification with convex loss function in the arbitrary class of real measurable functions. A general condition is given under which Neyman-Pearson classification with convex loss function has the same classifier as that with indicator loss function. We give analysis to NP-ERM with convex loss function and prove it＇s performance guarantees. An example of complexity penalty pair about convex loss function risk in terms of Rademacher averages is studied, which produces a tight PAC bound of the NP-ERM with convex loss function.
文摘The detection of partial discharge signals and classifying its patterns is an area of interest in the analysis of defects in high voltage cables. This paper investigates a filter-bank based approach to extract frequency domain based features to represent partial discharge signals. By applying the fast Fourier transform, the sampled partial discharge data are mapped into equivalent discrete frequency bins, which are then grouped into N equal sub-bands and also octave sub-bands, each providing N- dimensional features for partial discharge pattern classifi- cation. Two classifiers, namely, the support vector machine and the sparse representation classifier, are implemented and their outputs are fused, in order to improve the accuracy of classifying partial discharge. Classification accuracy is also compared with wavelet domain based octave frequency sub-band features.
