Clonal selection feature selection algorithm （CSFS） based on clonal selection algorithm （CSA）, a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-d...Clonal selection feature selection algorithm （CSFS） based on clonal selection algorithm （CSA）, a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm （FCSFS） for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager （PHI） and the airborne visible/infrared imaging spectrometer （AVlRIS）, respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.展开更多
Volcanic ash cloud has serious impacts on aviation.With volcanic ash dispersion,it also has a profound and long-term impact on climate and the environment.A new volcanic ash cloud detecting method (SWIR-TIR Volcanic A...Volcanic ash cloud has serious impacts on aviation.With volcanic ash dispersion,it also has a profound and long-term impact on climate and the environment.A new volcanic ash cloud detecting method (SWIR-TIR Volcanic Ash method,STVA) is presented that uses satellite images of Medium Resolution Spectral Imager (MERSI) and Visible and Infrared Radiometer (VIRR) on board the second generation Polar-Orbiting meteorological satellite of China (FY-3A).STVA is applied in detecting Iceland's Eyjafjallajokull volcano eruption.Compared with the traditional Split Window Temperature Difference method (SWTD),the results show that STVA is more sensitive to volcanic ash cloud than SWTD and can fairly extract volcanic ash information from the background of meteorological cloud and the ocean.Ash Radiance Index (ARI) and Absorbing Aerosol Index (AAI) derived from Metop-A satellite images are used to validate the performance of STVA.It is shown that STVA provides similar results with ARI and AAI.FY-3A/MERSI,VIRR and Terra /MODIS data are used to test STVA and SWTD.It is demonstrated that STVA derived from FY-3A satellite data is more effective in complicated meteorological conditions.This study shows great potential of using China's own new generation satellite data in future global volcanic ash cloud monitoring operation.展开更多
基金Supported by the Major State Basic Research Development Program (973 Program) of China (No. 2009CB723905)the National High Technology Research and Development Program (863 Program) of China (Nos.2009AA12Z114, 2007AA12Z148, 2007AA12Z181)+2 种基金the National Natural Sci-ence Foundation of China(Nos. 40771139,40523005, 40721001)the Research Fund for the Doctoral Program of Higher Education of China(No.200804861058)the Foundation of National Laboratory of Pattern Recognition
文摘Clonal selection feature selection algorithm （CSFS） based on clonal selection algorithm （CSA）, a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm （FCSFS） for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager （PHI） and the airborne visible/infrared imaging spectrometer （AVlRIS）, respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.
基金supported by National Basic Research Program of China (Grant No. 2010CB950700)
文摘Volcanic ash cloud has serious impacts on aviation.With volcanic ash dispersion,it also has a profound and long-term impact on climate and the environment.A new volcanic ash cloud detecting method (SWIR-TIR Volcanic Ash method,STVA) is presented that uses satellite images of Medium Resolution Spectral Imager (MERSI) and Visible and Infrared Radiometer (VIRR) on board the second generation Polar-Orbiting meteorological satellite of China (FY-3A).STVA is applied in detecting Iceland's Eyjafjallajokull volcano eruption.Compared with the traditional Split Window Temperature Difference method (SWTD),the results show that STVA is more sensitive to volcanic ash cloud than SWTD and can fairly extract volcanic ash information from the background of meteorological cloud and the ocean.Ash Radiance Index (ARI) and Absorbing Aerosol Index (AAI) derived from Metop-A satellite images are used to validate the performance of STVA.It is shown that STVA provides similar results with ARI and AAI.FY-3A/MERSI,VIRR and Terra /MODIS data are used to test STVA and SWTD.It is demonstrated that STVA derived from FY-3A satellite data is more effective in complicated meteorological conditions.This study shows great potential of using China's own new generation satellite data in future global volcanic ash cloud monitoring operation.
