Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object ima...Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes. Firstly,five fusion methods,i. e. IHS,Brovey,PCA,SFIM and Gram-Schmidt,were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally,in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image,the maximum likelihood classification( MLC) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity,and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details. The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods,and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest,the overall accuracy and kappa coefficient were 83. 14% and 0. 76,respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.展开更多
When evaluating the track fusion algorithm,common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one,and fail to effectively distingu...When evaluating the track fusion algorithm,common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one,and fail to effectively distinguish the performance of the algorithm when the state estimation is similar.Therefore,it is necessary to construct a high-resolution evaluation index,which can evaluate the track fusion algorithm more accurately,reasonably and comprehensively.Firstly,the advant ages and disadvantages of the optimal subpattern assignment(OSPA)dis tance as the accuracy index to evaluate the track fusion algorithm are analyzed.Then,its deficiencies are improved by using the Hellinger distance instead of the original Euclidean distance,and the distance is index transformed.Finally,a new evaluation index for track fusion algorithms is proposed,which is the OSPA distance based on Hellinger distance and index transformation.The simulation results show that the new index can not only correctly evaluate the fusion precision,but also consider the state uncertainty,making that can evaluate the track fusion algorithm more sensitively,and effectively solves the sensitivity of the index to the cut-off parameter c through index transformation.展开更多
In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data,but,not an appropriate fusion algorithm for anatomical and functional medical ima...In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data,but,not an appropriate fusion algorithm for anatomical and functional medical images.In this paper,the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively.When choosing high-frequency coefficients,the global gradient of each sub-image is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy,so that the fused image can reserve the anatomical image's edge and texture feature.Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.展开更多
基金Supported by Chinese National Natural Science Foundation(51208016)Beijing Natural Science Foundation(8122008)Beijing Education Commission Fund(KM201310005023)
文摘Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes. Firstly,five fusion methods,i. e. IHS,Brovey,PCA,SFIM and Gram-Schmidt,were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally,in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image,the maximum likelihood classification( MLC) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity,and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details. The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods,and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest,the overall accuracy and kappa coefficient were 83. 14% and 0. 76,respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.
基金the Natural Science Foundation of Hebei Province(No.F2017506006)。
文摘When evaluating the track fusion algorithm,common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one,and fail to effectively distinguish the performance of the algorithm when the state estimation is similar.Therefore,it is necessary to construct a high-resolution evaluation index,which can evaluate the track fusion algorithm more accurately,reasonably and comprehensively.Firstly,the advant ages and disadvantages of the optimal subpattern assignment(OSPA)dis tance as the accuracy index to evaluate the track fusion algorithm are analyzed.Then,its deficiencies are improved by using the Hellinger distance instead of the original Euclidean distance,and the distance is index transformed.Finally,a new evaluation index for track fusion algorithms is proposed,which is the OSPA distance based on Hellinger distance and index transformation.The simulation results show that the new index can not only correctly evaluate the fusion precision,but also consider the state uncertainty,making that can evaluate the track fusion algorithm more sensitively,and effectively solves the sensitivity of the index to the cut-off parameter c through index transformation.
基金The National High Technology Research and Development Program of China(‘863’Program)grant number:2007AA02Z4A9+1 种基金National Natural Science Foundation of Chinagrant number:30671997
文摘In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data,but,not an appropriate fusion algorithm for anatomical and functional medical images.In this paper,the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively.When choosing high-frequency coefficients,the global gradient of each sub-image is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy,so that the fused image can reserve the anatomical image's edge and texture feature.Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.
