An improved image registration method is proposed based on mutual infor- mation with hybrid optimizer. Firstly, mutual information measure is combined with morphological gradient information. The essence of the gradie...An improved image registration method is proposed based on mutual infor- mation with hybrid optimizer. Firstly, mutual information measure is combined with morphological gradient information. The essence of the gradient information is that locations a large gradient magnitude should be aligned, but also the orientation of the gradients at those locations should be similar. Secondly, a hybrid optimizer combined PSO with Powell algorithm is proposed to restrain local maxima of mutual information function and improve the registration accuracy to sub-pixel level. Lastly, muhlresolution data structure based on Mallat decomposition can not only improve the behavior of registration function, but also improve the speed of the algorithm. Experimental results demonstrate that the new method can yield good registration result, superior to traditional optimizer with respect to smoothness and attraction basin as well as convergence speed.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
文摘An improved image registration method is proposed based on mutual infor- mation with hybrid optimizer. Firstly, mutual information measure is combined with morphological gradient information. The essence of the gradient information is that locations a large gradient magnitude should be aligned, but also the orientation of the gradients at those locations should be similar. Secondly, a hybrid optimizer combined PSO with Powell algorithm is proposed to restrain local maxima of mutual information function and improve the registration accuracy to sub-pixel level. Lastly, muhlresolution data structure based on Mallat decomposition can not only improve the behavior of registration function, but also improve the speed of the algorithm. Experimental results demonstrate that the new method can yield good registration result, superior to traditional optimizer with respect to smoothness and attraction basin as well as convergence speed.
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.
