Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model an...Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.展开更多
Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matri...Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.展开更多
Due to the large quantities of data and high relativity of the spectra of remote sensing images, K-L transformation is used to eliminate the relativity. An improved ISODATA(Interative Self-Organizing Data Analysis Tec...Due to the large quantities of data and high relativity of the spectra of remote sensing images, K-L transformation is used to eliminate the relativity. An improved ISODATA(Interative Self-Organizing Data Analysis Technique A) algorithm is used to extract the spectrum features of the images. The computation is greatly reduced and the dynamic arguments are realized. The comparison of features between two images is carried out, and good results are achieved in simulation.展开更多
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
基金supported by the National Natural Science Foundation of China(41171336)the Project of Jiangsu Province Agricultural Science and Technology Innovation Fund(CX12-3054)
文摘Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.
基金supported by the Flood Control Foundation of Yellow River Conservancy Commissionthe 2007 Key Supporting Project on Undergraduate Graduation Thesis of North-western Polytechnical University.
文摘Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.
文摘Due to the large quantities of data and high relativity of the spectra of remote sensing images, K-L transformation is used to eliminate the relativity. An improved ISODATA(Interative Self-Organizing Data Analysis Technique A) algorithm is used to extract the spectrum features of the images. The computation is greatly reduced and the dynamic arguments are realized. The comparison of features between two images is carried out, and good results are achieved in simulation.
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.
