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
A new approach to the problem of registration of ultrasound images is presented, using a concept of Nonlinear Correlation Information Entropy (NCIE) as the matching criterion. The proposed method applies NCIE to measu...A new approach to the problem of registration of ultrasound images is presented, using a concept of Nonlinear Correlation Information Entropy (NCIE) as the matching criterion. The proposed method applies NCIE to measure the correlation degree between the image intensities of corresponding voxel in the floating and reference images. Registration is achieved by adjustment of the relative position until NCIE between the images is maximized. However, unlike mutual information (MI), NCIE varies in the closed interval [0, 1], and around the extremum it varies sharply, which makes it possible that thresholds of NCIE can be used to boost the search for the registration transformation. Using this feature of NCIE, we combine the downhill simplex searching algorithm to register the ultrasound images. The simulations are conducted to testify the effectiveness and rapidity of the proposed registration method, in which the ultrasound floating images are aligned to the reference images with required registration accuracy. Moreover, the NCIE based method can overcome local minima problem by setting thresholds and can take care of the differences in contrast between the floating and reference images.展开更多
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.
文摘A new approach to the problem of registration of ultrasound images is presented, using a concept of Nonlinear Correlation Information Entropy (NCIE) as the matching criterion. The proposed method applies NCIE to measure the correlation degree between the image intensities of corresponding voxel in the floating and reference images. Registration is achieved by adjustment of the relative position until NCIE between the images is maximized. However, unlike mutual information (MI), NCIE varies in the closed interval [0, 1], and around the extremum it varies sharply, which makes it possible that thresholds of NCIE can be used to boost the search for the registration transformation. Using this feature of NCIE, we combine the downhill simplex searching algorithm to register the ultrasound images. The simulations are conducted to testify the effectiveness and rapidity of the proposed registration method, in which the ultrasound floating images are aligned to the reference images with required registration accuracy. Moreover, the NCIE based method can overcome local minima problem by setting thresholds and can take care of the differences in contrast between the floating and reference images.
