In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perfor...In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images.The proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection performance.Compressed sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery.The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.展开更多
针对分布式雷达抗多假目标欺骗干扰问题,提出了一种基于数据级融合的抗干扰算法。首先,利用基于随机有限集的集势概率假设密度(Cardinalized Probability Hypothesis Density,CPHD)滤波器同时跟踪真实目标与假目标;然后,对于非协同假目...针对分布式雷达抗多假目标欺骗干扰问题,提出了一种基于数据级融合的抗干扰算法。首先,利用基于随机有限集的集势概率假设密度(Cardinalized Probability Hypothesis Density,CPHD)滤波器同时跟踪真实目标与假目标;然后,对于非协同假目标,根据其在分布式雷达检测中类似于虚警的性质,采用具有迫零特性的广义协方差交叉(Generalized Covariance Intersection,GCI)融合方法,在融合过程中实现干扰抑制;最后,对于协同假目标,引入雷达被动工作模式,采用一致性算法结合GCI方法进行分布式融合,达到了全局鉴假效果。仿真结果表明,在分布式雷达网络中,所提算法可以剔除非协同和协同假目标,并能有效跟踪真实目标。展开更多
基金Supported by National Natural Science Foundation of China （60874063） and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province （YJSCX2012-263HLJ）
基金Supported by the National Natural Science Foundation of China(No.61071163)
文摘In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images.The proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection performance.Compressed sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery.The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.
