Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hy...Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM). Global optimal search performance of PSO is improved by using a chaotic optimization search technique. Granularity based grid search strategy is used to optimize the SVM model parameters. Parameter optimization and classification of the SVM are addressed using the training date corre-sponding to the feature subset. A false classification rate is adopted as a fitness function. Tests of feature selection and classification are carried out on a hyperspectral data set. Classification performances are also compared among different feature extraction methods commonly used today. Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands. A feasible approach is provided for feature selection and classifica-tion of hyperspectral image data.展开更多
In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with mu...In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with multiple antennas, to make novel indoor geo-location method possible. In this paper, we presented a novel Least Square Support Vector Machine (LS-SVM) based data fusion algorithm to fuse signal strength measurements for indoor geo-location using only a single AP with MIMO arrays. We evaluate our proposed algorithms under indoor environments by MATLAB simulations. Simulation results show that our MIMO-based algorithm is superior to conventional least square algorithm.展开更多
基金Project 40401038 supported by the National Natural Science Foundation of China
文摘Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM). Global optimal search performance of PSO is improved by using a chaotic optimization search technique. Granularity based grid search strategy is used to optimize the SVM model parameters. Parameter optimization and classification of the SVM are addressed using the training date corre-sponding to the feature subset. A false classification rate is adopted as a fitness function. Tests of feature selection and classification are carried out on a hyperspectral data set. Classification performances are also compared among different feature extraction methods commonly used today. Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands. A feasible approach is provided for feature selection and classifica-tion of hyperspectral image data.
文摘In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with multiple antennas, to make novel indoor geo-location method possible. In this paper, we presented a novel Least Square Support Vector Machine (LS-SVM) based data fusion algorithm to fuse signal strength measurements for indoor geo-location using only a single AP with MIMO arrays. We evaluate our proposed algorithms under indoor environments by MATLAB simulations. Simulation results show that our MIMO-based algorithm is superior to conventional least square algorithm.
