The traditional synthetic aperture radar（SAR） image recognition techniques focus on the electro magnetic （EM） scattering centers, ignoring the important role of the shadow information on the SAR image recognition....The traditional synthetic aperture radar（SAR） image recognition techniques focus on the electro magnetic （EM） scattering centers, ignoring the important role of the shadow information on the SAR image recognition. It is difficult to classify targets by the shadow information independently, because the shadow shape is dependent on the radar aspect angle, the depression angle and the resolution. Moreover, the shadow shapes of different targets are similar. When the multiple SAR images of one target from different aspects are available, the performance of the target recognition can be improved. Aimed at the problem, a multi-aspect SAR image recognition technique based on the shadow information is developed. It extracts shadow profiles from SAR images, and takes chain codes as the feature vectors of targets. Then, feature vectors on multiple aspects of the same target are combined with feature sequences, and the hidden Markov model （HMM） is applied to the feature sequences for the target recognition. The simulation result shows the effectiveness of the method.展开更多
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.展开更多
For conventional optical polarization imaging of underwater target,the polarization degree of backscatter should be pre-measured by averaging the pixel intensities in the no target region of the polarization images,an...For conventional optical polarization imaging of underwater target,the polarization degree of backscatter should be pre-measured by averaging the pixel intensities in the no target region of the polarization images,and the polarization property of the target is assumed to be completely depolarized.When the scattering background is unseen in the field of view or the target is polarized,conventional method is helpless in detecting the target.An improvement is to use lots of co-polarization and cross polarization detection components.We propose a polarization subtraction method to estimate depolarization property of the scattering noise and target signal.And experiment in a quartz cuvette container is performed to demonstrate the effectiveness of the proposed method.The results show that the proposed method can work without scattering background reference,and further recover the target along with smooth surface for polarization preserving response.This study promotes the development of optical polarization imaging systems in underwater environments.展开更多
A successful case history of exploring for concealed structure using the high-resolution EM method in the investigation of the West-East Gas Pipeline Project＇s B Tunnel is presented in this paper. The high frequency ...A successful case history of exploring for concealed structure using the high-resolution EM method in the investigation of the West-East Gas Pipeline Project＇s B Tunnel is presented in this paper. The high frequency electromagnetic image system named STRATAGEM EH4, operating at frequencies ranging from 90KHz to 1Hz, was used for data acquisition. The orthogonal components of the electromagnetic field were measured during the field acquisition and the relevant electromagnetic attributes of the object body were extracted from the electromagnetic data. Hybrid sources, consisting of natural and full tensor-controlled sources, were utilized to produce high-quality electromagnetic field data. B Tunnel lies in the western part of Hubei province, at depths of less than 200m. The geologic setting of B tunnel is very complex. Following an initial geologic investigation, an outcrop considered to be a bedrock interface by investigators, collapsed during tunneling operations. A second investigation applied high-resolution EM and seismic refraction methods to reveal a more complex geologic structure along the tunnel route. The predicted rock classes and fault were encountered during the subsequent tunneling operations.展开更多
In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree alg...In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.展开更多
Data from abnormal channels in an imaging spectrometer almost always exerts an undesired impact on spectrum matching,classification,pattern recognition and other applications in hyperspectral remote sensing.To solve t...Data from abnormal channels in an imaging spectrometer almost always exerts an undesired impact on spectrum matching,classification,pattern recognition and other applications in hyperspectral remote sensing.To solve this problem,researchers should get rid of the data acquired by these channels.Selecting abnormal channels just in the way of visually examining each band image in a imaging data set is a conceivably hard and boring job.To relieve the burden,this paper proposes a method which exploits the spatial and spectral autocorrelations inherent in imaging spectrometer data,and can be used to speed up and,to a great degree,automate the detection of abnormal channels in an imaging spectrometer.This method is applied easily and successfully to one PHI data set and one Hymap data set,and can be applied to remotely sensed data from other hyperspectral sensors.展开更多
Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the ...Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.展开更多
文摘The traditional synthetic aperture radar（SAR） image recognition techniques focus on the electro magnetic （EM） scattering centers, ignoring the important role of the shadow information on the SAR image recognition. It is difficult to classify targets by the shadow information independently, because the shadow shape is dependent on the radar aspect angle, the depression angle and the resolution. Moreover, the shadow shapes of different targets are similar. When the multiple SAR images of one target from different aspects are available, the performance of the target recognition can be improved. Aimed at the problem, a multi-aspect SAR image recognition technique based on the shadow information is developed. It extracts shadow profiles from SAR images, and takes chain codes as the feature vectors of targets. Then, feature vectors on multiple aspects of the same target are combined with feature sequences, and the hidden Markov model （HMM） is applied to the feature sequences for the target recognition. The simulation result shows the effectiveness of the method.
基金Project(41374118)supported by the National Natural Science Foundation,ChinaProject(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China+3 种基金Project(2015M580700)supported by the China Postdoctoral Science Foundation,ChinaProject(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(2015JC3067)supported by the Hunan Provincial Science and Technology Program,ChinaProject(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.
基金National Natural Science Foundation of China(Nos.11847069,11847127)Science Foundation of North University of China(No.XJJ20180030)。
文摘For conventional optical polarization imaging of underwater target,the polarization degree of backscatter should be pre-measured by averaging the pixel intensities in the no target region of the polarization images,and the polarization property of the target is assumed to be completely depolarized.When the scattering background is unseen in the field of view or the target is polarized,conventional method is helpless in detecting the target.An improvement is to use lots of co-polarization and cross polarization detection components.We propose a polarization subtraction method to estimate depolarization property of the scattering noise and target signal.And experiment in a quartz cuvette container is performed to demonstrate the effectiveness of the proposed method.The results show that the proposed method can work without scattering background reference,and further recover the target along with smooth surface for polarization preserving response.This study promotes the development of optical polarization imaging systems in underwater environments.
基金The work is sponsored by National Natural Science Foundation of China (No. 40074036).
文摘A successful case history of exploring for concealed structure using the high-resolution EM method in the investigation of the West-East Gas Pipeline Project＇s B Tunnel is presented in this paper. The high frequency electromagnetic image system named STRATAGEM EH4, operating at frequencies ranging from 90KHz to 1Hz, was used for data acquisition. The orthogonal components of the electromagnetic field were measured during the field acquisition and the relevant electromagnetic attributes of the object body were extracted from the electromagnetic data. Hybrid sources, consisting of natural and full tensor-controlled sources, were utilized to produce high-quality electromagnetic field data. B Tunnel lies in the western part of Hubei province, at depths of less than 200m. The geologic setting of B tunnel is very complex. Following an initial geologic investigation, an outcrop considered to be a bedrock interface by investigators, collapsed during tunneling operations. A second investigation applied high-resolution EM and seismic refraction methods to reveal a more complex geologic structure along the tunnel route. The predicted rock classes and fault were encountered during the subsequent tunneling operations.
基金Projects 40401038 and 40871195 supported by the National Natural Science Foundation of ChinaNCET-06-0476 by the Program for New Century Excellent Talents in University20070290516 by the Specialized Research Fund for the Doctoral Program of Higher Education
文摘In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.
文摘Data from abnormal channels in an imaging spectrometer almost always exerts an undesired impact on spectrum matching,classification,pattern recognition and other applications in hyperspectral remote sensing.To solve this problem,researchers should get rid of the data acquired by these channels.Selecting abnormal channels just in the way of visually examining each band image in a imaging data set is a conceivably hard and boring job.To relieve the burden,this paper proposes a method which exploits the spatial and spectral autocorrelations inherent in imaging spectrometer data,and can be used to speed up and,to a great degree,automate the detection of abnormal channels in an imaging spectrometer.This method is applied easily and successfully to one PHI data set and one Hymap data set,and can be applied to remotely sensed data from other hyperspectral sensors.
基金Project supported by the National Natural Science Foundation of China （Nos. 61327902 and 61631009）
文摘Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.
