Multitemporal Images Change Detection Using Nonsubsampled Contourlet Transform and Kernel Fuzzy C-Means Clustering

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter. Then comes the fusion process, which uses an approach that combines NSCT and SR to fuse low frequency components. Similarly, the Max-absolute fusion rule is used to merge high frequency components. Finally, the final image is obtained through the disintegration of fused low and high frequency data. In terms of correlation coefficient, Entropy, spatial frequency, and fusion mutual information, our method outperforms other methods in terms of image quality enhancement and visual evaluation.

Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors

In order to improve the accuracy and computational efficiency of change detection of multi-temporal remote sensing images, a change detection algorithm based on nonsubsampled contourlet transform (NSCT) and independent component analysis (ICA) is proposed. The flexibility of NSCT in image decomposition and the effectiveness of ICA in image separation are used comprehensively. Firstly, multi-scale decomposition of remote sensing images is performed by NSCT. Then the decomposed low-frequency components and high-frequency components form into partitioned vectors. ICA is carried out for the partitioned vectors and separates mutual independent components. Next the separated components are transformed into image components which include the change image. Finally, change detection result is achieved by threshold segmentation and filtering of the change image. The experimental results show that, compared with the algorithm based on ICA, the algorithm based on wavelet transform and ICA, the proposed algorithm separates change information more effectively and reduces computational complexity. The obtained change image has higher accuracy and stronger robustness to the background.

There is always much difficult in the MR image segmentation. Although fuzzy c-means(FCM) clustering algorithm has been widely used in the field of image segmentation study, some inherent deficiencies of this algorithm especially the high cost of computation made the algorithm to be difficult widely used in practice. A novel algorithm, based on kernel fuzzy c-means (KFCM) clustering algorithm and the k-nearest neighbor (KNN) algorithm, is proposed to improve the performance of MR image segmentation. In this algorithm, the statistical gray level histogram of image is used in KFCM algorithm to speed up the algorithm. Furthermore, the spatial information of image is also considered by k-nearest neighbor algorithm based on kernel methods. With kernel methods each pixel of image is mapped into a high-dimensional feature space where FCM algorithm and KNN algorithm are carried out. Experiments show that the proposed algorithm is effective and efficient in image segmentation.

In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image is characterized by Gaussian mixture model, and the parameters of model are estimated by modified expectation maximization (EM) algorithm, which overcomes the classical EM algorithm drawbacks that easily trap in local maxima and be susceptible to initial value. According to the maximum a posterior theorem, we can get the pixel maximum posterior probability. We redefine the objective function of the KFCM algorithm which incorporates the pixel maximum posteriori probability, by minimizing the fuzzy objective function, the fuzzy segmentation algorithm is derived. The experimental results on a synthetic image and a real magnetic resonance image show that the proposed algorithm is more effective than the conventional FCM algorithm without local spatial constraints.

One of the most important aspects of image quality is contrast and this paper presents a new hybrid method for contrast enhancement. The proposed method is a combination of two basic contrast enhancement methods i.e. transform and histogram. At first we apply Nonsubsampled Contourlet Transform (NSCT) on the source image, then NSCT coefficients are mapped to fuzzy domain and modified by a mapping function in fuzzy domain. After transforming the modified membership values from fuzzy domain into frequency domain, the enhanced image is reconstructed from the modified NSCT coefficients by inverse NSCT. Finally, histogram of the image is equalized by Contrast Limited Adaptive Histogram Equalization (CLAHE). The experimental results show that our method can achieve higher improvement than both Contourlet Transform (CT) and Histogram Equalization (HE) contrast enhancement methods.

Due to the limitation of hardware, Infrared (IR) image has low-resolution (LR) and poor visual quality. Infrared image super-resolution (SR) is a good solution for this problem. However, the conventional SR methods have some drawbacks. Firstly, the trained dictionary is an unstructured dictionary, which may lead to worse results. Secondly, the representation of the image is too simple to effectively represent image. To resolve these problems, in this paper, firstly, the sparse dictionary is introduced into the IR image SR to get better results. Secondly, nonsubsampled contour let transform (NSCT) is employed in the proposed method to obtain a better representation of IR image. The experiment results indicate that the subjective visual effect and objective evaluation are acquired excellent performance in the proposed method. Besides, this method is superior to other methods in the paper.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

The nonsubsampled contourlet transform (NSCT) is a powerful and versatile tool that allows a multiresolution and directional representation to be achieved. In this paper, we propose an extension of two despeckling algorithms, proposed to restore SAR images and based on the undecimated separable wavelet transform, to work into the NSCT domain. The signal is modeled as affected by a multiplicative noise. The noise-free NSCT coefficients are estimated from the observed ones according to either the maximum-a-posteriori (MAP) or the linear minimum mean square error (LMMSE) criterion. The results show that the proposed restoration algorithms highly benefit from the fact of working into a multiresolution and multidirectional domain.

The suppression of random noise in seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) of seismic data is much lower which bring great difficulty to seismic data processing and interpretation. In this paper, we propose a new seismic random noise suppression approach based on the non-subsampled contourlet transform (NSCT). Firstly, the noisy seismic data is decomposed into different sub-bands of frequency and orientation responses using NSCT. Then, we use an appropriate thresholding operator to denoise the sub-bands of NSCT coefficients. Finally, we perform the inverse NSCT transform on the denoised NSCT components to reconstruct the denoised seismic data. We use synthetic data examples to demonstrate the superior performance of the proposed approach over the wavelet transform based threshold denoising method.

The project presents change detection approach for synthetic aperture radar (SAR) images based on an image fusion and supervised classifier system. The image fusion technique will be introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. NSCT (Non- subsampled contourlet transform) fusion rules based on an average operator and minimum local area gradient are chosen to fuse the contourlet coefficients for a low-frequency band and a high-frequency band respectively, to restrain the background information and to enhance the information of changed regions in the fused difference image. For the remote sensing images, differencing(subtraction operator) and ratioing (ratio operator) are well-known techniques for producing a difference image. In differencing, changes are measured by subtracting the intensity values pixel by pixel between the considered couple of temporal images. In ratioing, changes are obtained by applying a pixel-by-pixel ratio operator to the considered couple of temporal images. In the case of SAR images, the ratio operator is typically used instead of the subtraction operator since the image differencing technique is not adapted to the statistics of SAR images. An artificial neural network type multi-layer perceptron or back propagation with feed forward network will be proposed for classifying changed and unchanged regions in the fused difference image. This classifier comes under supervised segmentation which is worked based on training cum classification. The results will be proven that ratioing generates better difference image for change detection, using supervised classifier segmentation approach and efficiency of this algorithm will be exhibited by sensitivity and correlation evaluation.

We propose a novel and efficient SAR image despeckling via Bayesian shrinkage based on nonsubsampled contourlet transform, which has been recently introduced. Despeckling by means of contourlet transform introduce many visual artifacts due to the Gibbs-like phenomena. Nonsubsampled contour let transform is a flexible multiscale, multidirection and shift-invariant image decomposition that can be efficiently implemented via transform. A Bayesian estimator is applied to the decomposed contour let coefficients of the logarithmically transformed image to estimate the best value for the noise-free signal. Experimental results show that compared with conventional wavelet despeckling algorithm, the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserves image details, and the significant information of original image like textures and contour details is well maintained.

As the coupling and integration of multi-energy flow in the integrated energy system (IES) deepen increasingly, the cascading failure will develop across different energy systems more easily and widely through the energy hub (EH). And it brings great challenges to the security and reliability of IES. The defects of the present cascading failure model of IES have been summarized, and a novel search strategy of fault chains in IES combined heating and power network was proposed in this paper. First, the initial risk assessment index of each energy branch is proposed to form the initial fault sets. Then, combined heat and power control (CHPC) strategies are introduced to deal with the branch overload conditions during the cascading failure. What is more, in order to reduce the workload and overcome the limitation of present methods, we analyzed the relevance of the branches to be predicted by using the kernel fuzzy C-means (KFCM) clustering algorithm and select the branches with the highest value of relevance as the subsequent failure. Based on the predicted fault chain, vulnerability analysis is presented to locate critical component and find out the correlation between cascading outages. The comprehensive evaluation index is also established to effectively evaluate the impact severity of cascading failure. Finally, the case studies are carried out on the combined heating and power systems to demonstrate the effectiveness of the proposed method.

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm