To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is ba...To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.展开更多
A Mixed Line Rate(MLR)optical network is a good candidate for a core backbone network because of its ability to provide diverse line rates to effectively accommodate traffic demands with heterogeneous bandwidth requir...A Mixed Line Rate(MLR)optical network is a good candidate for a core backbone network because of its ability to provide diverse line rates to effectively accommodate traffic demands with heterogeneous bandwidth requirements.Because of the deleterious effects of physical impairments,there is a maximum transmission reach for optical signals before they have to be regenerated.Being expensive devices,regenerators are expected to be sparsely located and used in such a network,called a translucent optical network.In this paper,we consider the Grooming,Routing,and Wavelength Assignment(GRWA)problem so that the Quality of Transmission(QoT)for connections is satisfied,and the network-level performance metric of blocking probability is minimized.Cross-layer heuristics to effectively allocate the sparse regenerators in MLR networks are developed,and extensive simulation results are presented to demonstrate their effectiveness.展开更多
基金supported by the National Natural Science Foundation of China(No.61275010)the Ph.D.Programs Foundation of Ministry of Education of China(No.20132304110007)+1 种基金the Heilongjiang Natural Science Foundation(No.F201409)the Fundamental Research Funds for the Central Universities(No.HEUCFD1410)
文摘To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.
基金supported in part by National Science Foundation (NSF) under Grants No. CNS-0915795 and No.CNS-0916890
文摘A Mixed Line Rate(MLR)optical network is a good candidate for a core backbone network because of its ability to provide diverse line rates to effectively accommodate traffic demands with heterogeneous bandwidth requirements.Because of the deleterious effects of physical impairments,there is a maximum transmission reach for optical signals before they have to be regenerated.Being expensive devices,regenerators are expected to be sparsely located and used in such a network,called a translucent optical network.In this paper,we consider the Grooming,Routing,and Wavelength Assignment(GRWA)problem so that the Quality of Transmission(QoT)for connections is satisfied,and the network-level performance metric of blocking probability is minimized.Cross-layer heuristics to effectively allocate the sparse regenerators in MLR networks are developed,and extensive simulation results are presented to demonstrate their effectiveness.
