Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification result...Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.展开更多
In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, whi...In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.展开更多
Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are ...Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are some technical difficulties in lift fin stabilizers,such as lift force detection and lift force sensor installation,so it cannot achieve the good antirolling performance.Therefore,a fin stabilizer system with fin-lift/fin-angle integrated control is brought forward.Data fusion based on wavelet denoising technology is employed in the system,which combines lift with fin angle local information from two sensors with different frequency ranges in order to eliminate redundant and contradictory information,and using complementary information to obtain the relative integrity of the lift force signal.The system model is established in this paper,and the fusion signal and the antirolling performance of this model are simulated respectively.The result shows that the control system can meet the antirolling need in different sea situations.展开更多
A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coef...A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.展开更多
In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put...In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put forward in the paper.Compared to the wavelet transform,the wavelet packet transform is more intricate and effective for the medical image fusion.As indicated by the experimental results,parameters of the feedback system of the new algorithm are significantly superior to those of the wavelet transform,with practicability and accuracy.展开更多
Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusi...Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusing low and high frequency sub-images. Then, the "coefficient absolute value" that can provide clear and detail parts is adapted to fuse high-frequency coefficients, where as the "region energy ratio" which can efficiently preserve most information of source images is employed to fuse low-frequency coefficients. Finally, the fused image is reconstructed by inverse wavelet transform. Results Visually and quantitatively experimental results indicate that the proposed fusion method is superior to traditional wavelet transform and the existing fusion methods. Conclusion The proposed method is a feasible approach for multimodal medical image fusion which can obtain more efficient and accurate fusions results even in the noise environment.展开更多
This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficie...This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.展开更多
With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet tran...With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.展开更多
According to the time-frequency localization characteristic of the wavelet transform (WT)and the nonlinear reflection of the neural network,this paper presents the neural network data fusion fault diagnosis method bas...According to the time-frequency localization characteristic of the wavelet transform (WT)and the nonlinear reflection of the neural network,this paper presents the neural network data fusion fault diagnosis method based on wavelet transform.The network construction and the signal processing steps are introduced in detail.The correct result was attained by using this method in rotary machinery fault diagnosis.It proves the method efficient in fault diagnosis, which is expected to have a wide application.展开更多
In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the ...In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.展开更多
The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and n...The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.展开更多
Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imag...Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.展开更多
Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across ...Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across many spheres of influence such as business, finance, medicine, etc. to enhance produces, causes, efficiency, etc. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep Learning Algorithms. This article used Convolutional Neural Networks (CNN) to classify scenes in the CIFAR-10 database, and detect emotions in the KDEF database. The proposed method converted the data to the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high frequency features, and processing the fused feature mapping resulted in an advance in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings revealed a substantial increase in accuracy.展开更多
In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification probl...In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.展开更多
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
文摘Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.
基金Sponsored by the Nation Nature Science Foundation of China(Grant No.61275010,61201237)the Fundamental Research Funds for the Central Universities(Grant No.HEUCFZ1129,No.HEUCF120805)
文摘In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.
基金supported by the "Ship Control Engineering" Emphasis Project of 211 Engineering in the Tenth Five-Year Plan
文摘Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are some technical difficulties in lift fin stabilizers,such as lift force detection and lift force sensor installation,so it cannot achieve the good antirolling performance.Therefore,a fin stabilizer system with fin-lift/fin-angle integrated control is brought forward.Data fusion based on wavelet denoising technology is employed in the system,which combines lift with fin angle local information from two sensors with different frequency ranges in order to eliminate redundant and contradictory information,and using complementary information to obtain the relative integrity of the lift force signal.The system model is established in this paper,and the fusion signal and the antirolling performance of this model are simulated respectively.The result shows that the control system can meet the antirolling need in different sea situations.
文摘A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.
文摘In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put forward in the paper.Compared to the wavelet transform,the wavelet packet transform is more intricate and effective for the medical image fusion.As indicated by the experimental results,parameters of the feedback system of the new algorithm are significantly superior to those of the wavelet transform,with practicability and accuracy.
文摘Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusing low and high frequency sub-images. Then, the "coefficient absolute value" that can provide clear and detail parts is adapted to fuse high-frequency coefficients, where as the "region energy ratio" which can efficiently preserve most information of source images is employed to fuse low-frequency coefficients. Finally, the fused image is reconstructed by inverse wavelet transform. Results Visually and quantitatively experimental results indicate that the proposed fusion method is superior to traditional wavelet transform and the existing fusion methods. Conclusion The proposed method is a feasible approach for multimodal medical image fusion which can obtain more efficient and accurate fusions results even in the noise environment.
文摘This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.
文摘With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.
文摘According to the time-frequency localization characteristic of the wavelet transform (WT)and the nonlinear reflection of the neural network,this paper presents the neural network data fusion fault diagnosis method based on wavelet transform.The network construction and the signal processing steps are introduced in detail.The correct result was attained by using this method in rotary machinery fault diagnosis.It proves the method efficient in fault diagnosis, which is expected to have a wide application.
文摘In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.
文摘The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.
文摘Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.
文摘Machine learning is an integral technology many people utilize in all areas of human life. It is pervasive in modern living worldwide, and has multiple usages. One application is image classification, embraced across many spheres of influence such as business, finance, medicine, etc. to enhance produces, causes, efficiency, etc. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep Learning Algorithms. This article used Convolutional Neural Networks (CNN) to classify scenes in the CIFAR-10 database, and detect emotions in the KDEF database. The proposed method converted the data to the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high frequency features, and processing the fused feature mapping resulted in an advance in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings revealed a substantial increase in accuracy.
基金Supported in part by Science & Technology Department of Shanghai (05dz15004)
文摘In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.
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.
