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Tan Tie-niu - One of the best experts on this subject based on the ideXlab platform.

  • Image scrambling effect evaluation method based on mutual information distance of Difference Image
    Journal of Computer Applications, 2009
    Co-Authors: Tan Tie-niu
    Abstract:

    The new Image scrambling effect evaluation method based on mutual information distance of Difference Image was proposed.The new concept of mutual information distance based on mutual information was firstly proposed,and then the Difference Image of the original Image and scrambled Image was obtained by means of Difference operation on two Images.At last the mutual information of two Difference Images was computed and acted as the new criteria of Image scrambling effect evaluation.Experimental results show that the proposed method is effective to describe the relation between the scrambling effect and the number of iterations in the scrambling techniques,which is largely consistent with human vision.For different Images,when some transformation is used,this evaluation method can reflect to some extent the scrambling effects in each scrambling stage.

  • Study on Image scrambling effect based on mean square signal-to-noise rate of Difference Image
    Computer Engineering and Design, 2009
    Co-Authors: Tan Tie-niu
    Abstract:

    Considering that the mean square signal-to-noise rate based Image scrambling degree evaluation method has not been capable of reflecting the Image scrambling result good or bad in actual fact. First, the Difference Images of the original Image and scrambled Image is obtained by means of Difference operation on two Images, then the mean square signal-to-noise rate of two Difference Images is acted as the new criteria of Image scrambling effect evaluation. New Image scrambling effect evaluation method based on Difference Image combined with the mean square signal-to-noise rate is proposed. Experimental results show that the proposed method is effective to describe the relation between the scrambling effect and the number of iterations in the scrambling techniques, which largely consists with human vision.

Zhiqiang Zhou - One of the best experts on this subject based on the ideXlab platform.

  • change detection in synthetic aperture radar Images based on Image fusion and fuzzy clustering
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Maoguo Gong, Zhiqiang Zhou
    Abstract:

    This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) Images based on an Image fusion strategy and a novel fuzzy clustering algorithm. The Image fusion technique is introduced to generate a Difference Image by using complementary information from a mean-ratio Image and a log-ratio Image. In order to restrain the background information and enhance the information of changed regions in the fused Difference Image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused Difference Image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR Images show that the Image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

Biao Hou - One of the best experts on this subject based on the ideXlab platform.

  • using combined Difference Image and k means clustering for sar Image change detection
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Yaoguo Zheng, Xiangrong Zhang, Biao Hou, Ganchao Liu
    Abstract:

    In this letter, a simple and effective unsupervised approach based on the combined Difference Image and k-means clustering is proposed for the synthetic aperture radar (SAR) Image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR Images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the k-means clustering algorithm with k = 2 is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the Difference Image are considered in this method. Experimental results obtained on four real SAR Image data sets confirm the effectiveness of the proposed approach.

  • Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Biao Hou, Yaoguo Zheng, Qian Wei, Shuang Wang
    Abstract:

    Multitemporal synthetic aperture radar (SAR) Images have been successfully used for the detection of different types of terrain changes. SAR Image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR Images based on Image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a Difference Image. In order to obtain a better Difference map, Image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio Difference Image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused Difference Image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR Image change detection in terms of shape preservation of the detected change portion and the numerical results.

Yaoguo Zheng - One of the best experts on this subject based on the ideXlab platform.

  • using combined Difference Image and k means clustering for sar Image change detection
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Yaoguo Zheng, Xiangrong Zhang, Biao Hou, Ganchao Liu
    Abstract:

    In this letter, a simple and effective unsupervised approach based on the combined Difference Image and k-means clustering is proposed for the synthetic aperture radar (SAR) Image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR Images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the k-means clustering algorithm with k = 2 is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the Difference Image are considered in this method. Experimental results obtained on four real SAR Image data sets confirm the effectiveness of the proposed approach.

  • Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Biao Hou, Yaoguo Zheng, Qian Wei, Shuang Wang
    Abstract:

    Multitemporal synthetic aperture radar (SAR) Images have been successfully used for the detection of different types of terrain changes. SAR Image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR Images based on Image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a Difference Image. In order to obtain a better Difference map, Image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio Difference Image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused Difference Image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR Image change detection in terms of shape preservation of the detected change portion and the numerical results.

Turgay Celik - One of the best experts on this subject based on the ideXlab platform.

  • multitemporal Image change detection using undecimated discrete wavelet transform and active contours
    IEEE Transactions on Geoscience and Remote Sensing, 2011
    Co-Authors: Turgay Celik
    Abstract:

    In this paper, an unsupervised change detection method for satellite Images is proposed. Owing to its robustness against noise, the undecimated discrete wavelet transform is exploited to obtain a multiresolution representation of the Difference Image, which is obtained from two satellite Images acquired from the same geographical area but at different time instances. A region-based active contour model is then applied to the multiresolution representation of the Difference Image for segmenting the Difference Image into the “changed” and “unchanged” regions. The proposed change detection method has been conducted on two types of Image data sets, i.e., the synthetic aperture radar Images and the optical Images. The change detection results are compared with several state-of-the-art techniques. The extensive simulation results clearly show that the proposed change detection method consistently yields superior performance.

  • unsupervised change detection in satellite Images using principal component analysis and k means clustering
    IEEE Geoscience and Remote Sensing Letters, 2009
    Co-Authors: Turgay Celik
    Abstract:

    In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite Images using principal component analysis (PCA) and k-means clustering. The Difference Image is partitioned into h times h nonoverlapping blocks. S, S les h2, orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block set to create an eigenvector space. Each pixel in the Difference Image is represented with an S-dimensional feature vector which is the projection of h times h Difference Image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means clustering with k = 2 and then assigning each pixel to the one of the two clusters by using the minimum Euclidean distance between the pixel's feature vector and mean feature vector of clusters. Experimental results confirm the effectiveness of the proposed approach.