为了解决传统代数计算法构造的差异图背景中含有较多噪点的问题,提高变化检测的精度,引入信息论中相对熵的概念,借助邻域处理,提出了一种基于邻域相对熵的差异图构造方法,并应用模糊局部信息C均值(fuzzy local information C-means,FLI...为了解决传统代数计算法构造的差异图背景中含有较多噪点的问题,提高变化检测的精度,引入信息论中相对熵的概念,借助邻域处理,提出了一种基于邻域相对熵的差异图构造方法,并应用模糊局部信息C均值(fuzzy local information C-means,FLICM)非监督聚类算法,实现变化信息的自动提取。通过采用4组单极化前后时相SAR影像数据集,分析对比了不同邻域形式的相对熵差异图和传统差异图的检测性能。实验结果表明,应用该方法生成的差异影像,对噪声有着较强的鲁棒性,能够满足变化检测的需求,且在定量评价的性能指标方面表现较好。其中,基于D-邻域相对熵差异图进行变化检测的结果更加突出。展开更多
A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search （3DRS） algorithm is used in order to obtain motion information accurately. A motion feature descrip...A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search （3DRS） algorithm is used in order to obtain motion information accurately. A motion feature descriptor （MFD） is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm （FCM） is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.展开更多
为了进一步提高多时相遥感图像变化检测的精度,本文提出了一种将Shearlet变换与核主成分分析(kernel principal component analysis,KPCA)相结合用于遥感图像变化检测的算法.首先利用Shearlet变换的多尺度、多方向和各向异性等特点,对...为了进一步提高多时相遥感图像变化检测的精度,本文提出了一种将Shearlet变换与核主成分分析(kernel principal component analysis,KPCA)相结合用于遥感图像变化检测的算法.首先利用Shearlet变换的多尺度、多方向和各向异性等特点,对遥感图像进行多尺度分解,然后对分解后的数据进行核主成分分析,再进行Shearlet反变换得到含变化信息的图像,最后对该图像利用模糊局部信息C均值(fuzzy local information c-means,FLICM)聚类算法进行分割,实现遥感图像的变化检测.大量试验结果表明,与基于主成分分析(principal component analysis,PCA)、基于KPCA、基于小波变换和PCA 3种变化检测算法相比,本文算法能有效地分离出变化信息,得到更准确的变化检测图像,具有更高的变化检测精度,且对背景有较强的鲁棒性,同时也减少了计算复杂度.展开更多
This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effect...This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.展开更多
文摘为了解决传统代数计算法构造的差异图背景中含有较多噪点的问题,提高变化检测的精度,引入信息论中相对熵的概念,借助邻域处理,提出了一种基于邻域相对熵的差异图构造方法,并应用模糊局部信息C均值(fuzzy local information C-means,FLICM)非监督聚类算法,实现变化信息的自动提取。通过采用4组单极化前后时相SAR影像数据集,分析对比了不同邻域形式的相对熵差异图和传统差异图的检测性能。实验结果表明,应用该方法生成的差异影像,对噪声有着较强的鲁棒性,能够满足变化检测的需求,且在定量评价的性能指标方面表现较好。其中,基于D-邻域相对熵差异图进行变化检测的结果更加突出。
基金Supported by the National Natural Science Foundation of China （No. 60772134, 60902081, 60902052） the 111 Project （No.B08038） the Fundamental Research Funds for the Central Universities（No.72105457）.
文摘A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search （3DRS） algorithm is used in order to obtain motion information accurately. A motion feature descriptor （MFD） is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm （FCM） is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.
文摘为了进一步提高多时相遥感图像变化检测的精度,本文提出了一种将Shearlet变换与核主成分分析(kernel principal component analysis,KPCA)相结合用于遥感图像变化检测的算法.首先利用Shearlet变换的多尺度、多方向和各向异性等特点,对遥感图像进行多尺度分解,然后对分解后的数据进行核主成分分析,再进行Shearlet反变换得到含变化信息的图像,最后对该图像利用模糊局部信息C均值(fuzzy local information c-means,FLICM)聚类算法进行分割,实现遥感图像的变化检测.大量试验结果表明,与基于主成分分析(principal component analysis,PCA)、基于KPCA、基于小波变换和PCA 3种变化检测算法相比,本文算法能有效地分离出变化信息,得到更准确的变化检测图像,具有更高的变化检测精度,且对背景有较强的鲁棒性,同时也减少了计算复杂度.
基金supported by the Student’s Platform for Innovation and Entrepreneurship Training Program(No.201510060022)
文摘This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.
