Synthetic Aperture Radar Image Change Detection Research Articles

Feature-based non-parametric estimation of Kullback–Leibler divergence for SAR image change detection

ABSTRACTIn this article, a method based on a non-parametric estimation of the Kullback–Leibler divergence using a local feature space is proposed for synthetic aperture radar (SAR) image change detection. First, local features based on a set of Gabor filters are extracted from both pre- and post-event images. The distribution of these local features from a local neighbourhood is considered as a statistical representation of the local image information. The Kullback–Leibler divergence as a probabilistic distance is used for measuring the similarity of the two distributions. Nevertheless, it is not trivial to estimate the distribution of a high-dimensional random vector, let alone the comparison of two distributions. Thus, a non-parametric method based on k-nearest neighbour search is proposed to compute the Kullback–Leibler divergence between the two distributions. Through experiments, this method is compared with other state-of-the-art methods and the effectiveness of the proposed method for SAR image change detection is demonstrated.

Unsupervised SAR Image Change Detection Based on SIFT Keypoints and Region Information

This letter presents a new unsupervised distribution-free change detection method for synthetic aperture radar (SAR) images based on scale-invariant feature transform (SIFT) keypoints and region information. Since the SIFT can detect bloblike structures in an image and be insensitive to noise, we first extract noise-robust SIFT keypoints in the log-ratio image to reduce the detection range. Then, in order to obtain accurate changed regions, rather than directly obtaining the change-detection map from the difference image as in some traditional change detection methods, we make segmentation around the extracted keypoints in the two original multitemporal SAR images, where the edges of detection regions are much clearer than those in the difference image, and further compare the two segmentations to generate the change-detection map. This method utilizes the bloblike structure information offered by SIFT keypoints and the region information extracted via image segmentation. Experiments on real SAR images demonstrate the effectiveness of the proposed method.

Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation

ABSTRACTIn this article, we propose a novel unsupervised change detection method for synthetic aperture radar (SAR) images. First, we generate a difference image as a weighted average of a log-ratio image and a mean-ratio image, which has the advantage of enhancing the information of changed regions and restraining the information of unchanged background regions simultaneously. Second, we propose a variational soft segmentation model based on non-differentiable curvelet regularization and L1-norm fidelity. Numerically, by using the split Bregman technique for curvelet regularization term and reformulating the L1-norm fidelity as weighted L2-norm fidelity, we get an effective algorithm in which each sub-problem has a closed-form solution. The numerical experiments and comparisons with several existing methods show that the proposed method is promising, with not only high robustness to non-Gaussian noise or outliers but also high change detection accuracy. Moreover, the proposed method is good at detecting fine-structured change areas. Especially, it outperforms other methods in preserving edge continuity and detecting curve-shaped changed areas.

SAR Image Change Detection Based on Multiple Kernel K-Means Clustering With Local-Neighborhood Information

Performance of the k-means clustering algorithm for synthetic aperture radar (SAR) image change detection is usually worsened by the inherent existence of the speckle noise. Therefore, in this letter, an unsupervised multiple kernel k-means clustering algorithm with local-neighborhood information ( LIMKKM algorithm) is proposed for SAR image change detection. The LIMKKM algorithm contributes in two aspects. First, it fuses various features through a weighted summation kernel by automatically and optimally computing the kernel weights. Here, the intensity and texture features of the ratio image are fused. Second, it incorporates the local-neighborhood information into its clustering objective function for providing strong noise immunity. The LIMKKM change detection algorithm is carried out in a train–test way to lighten the computational burden. Experimental results on real images demonstrate the effectiveness, especially the strong noise immunity, of the LIMKKM method and illustrate that it is suitable for SAR image change detection.

A Change Detection Method of Multi-temporal SAR Images Based on Contourlet Transform

Synthetic aperture radar (SAR) imaging is very sensitive to direction, so the information that SAR images contain is often not completely same from different directions. The information obtained from multi-directions must be more abundant and more accurate than that of from a single direction in a SAR image. The Contourlet transform is a multi-scale geometric analysis theory, holding many advantages for signal processing, such as multi-resolution, multi-direction and anisotropy; therefore, it is in favor of extracting different direction information for SAR images. According to the directional sensitivity of SAR imaging and the characteristics of multi-scale and multi direction to the Contourlet transform, this paper proposed a new SAR image change detection method based on Contourlet transform, called CTCD algorithm. Using the multi-direction characteristic of Contourlet transform, the CTCD method can get more accurate changed information for multi-temporal SAR images. The practical SAR image data is employed to test the CTCD algorithm and results show that the CTCD algorithm is a feasible change detection algorithm for multi-temporal SAR images, and it can obtain more abundant and more accurate information than the direct difference change detection (DDCD) algorithm.

A multiobjective fuzzy clustering method for change detection in SAR images

On account of the presence of speckle noise, the trade-off between removing noise and preserving detail is crucial for the change detection task in Synthetic Aperture Radar (SAR) images. In this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. The change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. We optimize the two constructed objective functions simultaneously by using a multiobjective fuzzy clustering method, which updates the membership values according to the weights of the two objectives to find the optimal trade-off. The proposed method obtains a set of solutions with different trade-off relationships between the two objectives, and users can choose one or more appropriate solutions according to requirements for diverse problems. Experiments conducted on real SAR images demonstrate the superiority of the proposed method.

SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture

Kernel methods with specifically designed kernel function are suitable for dealing with practical nonlinear problems. However, kernel methods have found limited applications to synthetic aperture radar (SAR) image change detection in that their performances are affected by the inherent multiplicative speckle noise of SAR images. It is known that the spatial-contextual information is helpful in suppressing the degrading effects of the noise. Therefore, a label-information composite kernel ( LIC kernel ) constructed on the basis of the spatial-contextual information is proposed in this paper for SAR image change detection. A typical spatial information, the output-space label-neighborhood information that is extracted using all labels in the neighborhood of each pixel, may enhance noise immunity, but with inaccurate edge locations simultaneously. Consequently, the anisotropic Gaussian kernel model is utilized for analyzing anisotropic textures of the bitemporal images, and then, a comparison scheme acting on the input-space textures of the bi-temporal images is proposed to supervise the extraction of the output-space label-neighborhood information in the construction of the LIC kernel . The constructed LIC kernel is of good preservation of edge locations of changed areas as well as strong noise immunity. The LIC kernel is updated iteratively with the newest change map outputted from the support vector machine, until the change map converges. Experiments on real SAR images demonstrate the effectiveness of the LIC kernel method and illustrate that it has both strong noise immunity and good preservation of edge locations of changed areas for SAR image change detection.

Multipolarimetric SAR image change detection based on multiscale feature-level fusion

Abstract. Many methodologies of change detection have been discussed in the literature, but most of them are tested on only optical images or traditional synthetic-aperture radar (SAR) images. Few studies have investigated multipolarimetric SAR image change detection. In this study, we presented a type of multipolarimetric SAR image change detection approach based on nonsubsampled contourlet transform and multiscale feature-level fusion techniques. In this approach, Instead of denoising an image in advance, the nonsubsampled contourlet transform multiscale decomposition was used to reduce the effect of speckle noise by processing only the low-frequency sub-band coefficients of the decomposed image, and the multiscale feature-level fusion technique was employed to integrate the rich information obtained from various polarization images. Because SAR image information is dependent on scale, a multiscale multipolarimetric feature-level fusion strategy is introduced into the change detection to improve change detection precision; this feature-level fusion can not only achieve complementation of information with different polarizations and on different scales, but also has better robustness against noise. Compared with PCA methods, the proposed method constructs better differential images, resulting in higher change detection precision.

Environmental monitoring of natural disasters using synthetic aperture radar image multi-directional characteristics

Synthetic aperture radar (SAR) imaging can penetrate rain, snow, fog, and mist, providing an effective precipitation detector under a wide range of atmospheric conditions. This powerful technique operates under any weather conditions and can detect environmental changes or help in the evaluation of natural disasters. Multi-temporal change detection is important for monitoring disasters, but commonly applied wavelet transforms are not ideal for capturing change information. This paper presents a new multi-directional change detection (MDCD) method designed to improve the change detection accuracy using multiple SAR images. This method employs double-density dual-tree complex wavelet transforms (DDDT-CWT) theory, which allows the capture of multi-directional information. The MDCD method can provide information for 16 directions at any decomposed scale, which allows for change detection in multiple SAR images collected over time. We used the MDCD algorithm method to analyse SAR images from actual natural disasters and successfully identified environmental changes over time using SAR images.

SAR Image Change Detection Based on Hybrid Conditional Random Field

In this letter, we propose a hybrid conditional random field (HCRF) model for synthetic aperture radar (SAR) image change detection. The HCRF model is constructed by incorporating the statistics of the log-ratio image derived from the two-temporal SAR images into conditional random field model. In this way, it is able to integrate the SAR images information, including the texture features of the two-temporal SAR images, the statistics, and the spatial interactions of the log-ratio image, into the change detection. Moreover, to achieve the integration of the information, the HCRF model consists of three parts, namely, the unary potential, the pairwise potential, and the data term modeled by the statistics of the log-ratio image. The unary potential is modeled by a support vector machine using the texture features extracted from the two-temporal SAR images, and the pairwise potential is constructed by the multilevel logistical model to capture the spatial interactions of the log-ratio image. Generalized Gamma distribution (GΓD) is utilized to model the statistics of the intensity data in the log-ratio image. Finally, experimental results on three sets of two-temporal SAR images validate the effectiveness of the proposed HCRF model.

Change Detection Based on a Transductive Inference in SAR Images

As the change detection based on Synthetic Aperture Radar (SAR) images that are difficult and very limited to acquire labeled samples are of low detection rate and high error rate, Thus a progressive transductive SVM algorithm based on original feature space for unsupervised change detection of SAR images is proposed. The pseudo-training set of the difference image is obtained using K-means clustering method without any prior information; Starting from these initial seeds, the progressive transductive SVM performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a transductive inference algorithm. Using dynamic region labeling rule, the algorithm not only achieves its rules of progressive labeling and dynamic adjusting, but also raises its speed at the same time. Experimental results obtained on different multitemporal SAR images show that, transductive inference algorithm that extract the information of unlabeled patterns improve the SVM classifier accuracy. These results confirm the effectiveness of the proposed approach.

Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images

In this paper, we establish a deep neural network using stacked Restricted Boltzmann Machines (RBMs) to analyze the difference images and detect changes between multitemporal synthetic aperture radar (SAR) images. Given the two multitemporal images, a difference image which shows difference degrees between corresponding pixels is generated. Then, RBMs are stacked to form a deep hierarchical neural network to learn to analyze the difference image and recognize the changed pixels and unchanged pixels. The learning process includes unsupervised layer-wise feature learning and supervised fine-tuning of network parameters. Unsupervised learning aims to learn the representation of the difference image. Supervised fine-tuning aims to learn to classify the changed and unchanged pixels. The network can learn from datasets that have few labeled data. The labeled data can be selected from the results obtained by other methods because there is no prior information in image change detection. The system learns to detect the changes instead of recognizing the changes by fixed equations as in traditional change detection algorithms. We test the network with real synthetic aperture radar datasets and the labeled samples are extracted from the results obtained, respectively, by several methods, including a thresholding method, a level set method and two clustering methods. The results achieved by the trained network outperform that of other methods.

Semisupervised SAR Image Change Detection Using a Cluster-Neighborhood Kernel

Change detection can be performed in a supervised manner. However, supervised methods for synthetic aperture radar (SAR) image change detection may suffer from lack of training samples. Therefore, in this letter, a semisupervised support vector machine classifier based on a cluster-neighborhood (CN) kernel is proposed for SAR image change detection. In the proposed method, samples are categorized into two neighborhoods with kernel k-means clustering algorithm. In addition, a CN kernel is constructed based on the composite-ratio kernel using the neighborhood-based statistical features. When a few labeled samples are available, the proposed CN kernel explores the information of unlabeled samples to enhance its discriminative ability and enhance its robustness against speckles. Experimental results on real SAR image change detection demonstrate the effectiveness of the proposed method when a few labeled samples are available.

Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection

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.

Change Detection in High-Resolution SAR Images Based on Jensen–Shannon Divergence and Hierarchical Markov Model

This paper addresses the problem of change detection in high-resolution multitemporal synthetic aperture radar (SAR) images. We propose to use Jensen-Shannon divergence (JSD) to measure the dissimilarity of the two scenes acquired at different times for deriving the difference map (DM). We figure out this divergence in a nonparametric way by introducing a direct density ratio estimation, making the DM generation free of distribution assumption. We also present a multiscale change detection framework which can capture and combine change cues at different scales. First, the coregistered SAR image pairs are decomposed into different scales by multiscale decimated wavelet transform (DWT). Next, the DM in each scale is generated by computing the local JSD. These DMs are then represented by a hierarchical Markovian model based on a quad-tree structure. The change map is finally inferred relying on hierarchical marginal posterior mode (HMPM). Experimental results on multitemporal TerraSAR-X images demonstrate the effectiveness of the proposed approach.

A variational change detection method for multitemporal SAR images

In this letter, we develop a variational model for change detection in multitemporal synthetic aperture radar (SAR) images. SAR images are typically polluted by multiplicative noise, therefore ordinary active contour model (ACM), or the snake model, for image segmentation is not suitable for change detection in multitemporal SAR images. Our model is a generalization of ACM under the assumption that the image data fits the Generalized Gaussian Mixture (GGM) model. Our method first computes the log-ratio image of the input multitemporal SAR images. Then the method iteratively executes the following two steps until convergence: (1) estimate the parameters for the generalized Gaussian distributions inside and outside the current evolving curve using maximum-likelihood estimation; (2) evolve the current curve according to the image data and the parameters previously estimated. When convergence is achieved, the location of the evolving curve depicts the changed and the unchanged areas.Experiments were carried out on both semi-simulated data set and real data set. Results showed that the proposed method achieves total error rates of 0.43% and 1.05%, for semi-simulated and real data sets, respectively, which were comparable to other prevalent methods.

Using Combined Difference Image and $k$ -Means Clustering for SAR Image Change Detection

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.

Change detection in SAR images by artificial immune multi-objective clustering

This paper addresses the problem of unsupervised change detection in Synthetic Aperture Radar (SAR) images. Previous approaches have used evolutionary clustering optimization methods, which can suffer from reduced accuracy, because they often use only a single objective function and can easily become trapped at locally optimal values. To overcome these difficulties, we propose a new approach which combines the artificial immune system (AIS) theory with a multi-objective optimization algorithm. First, the self-adaptive artificial immune multi-objective algorithm is adopted to pre-sort the difference image. During this procedure, the difference image is categorized into three classes – changed class, unchanged class and uncertain samples. Second, based on wavelet decomposition to extract features from the difference image, the immune clonal multi-objective clustering algorithm is used to search for the optimal clustering centers of uncertain samples, labeling them as changed or unchanged. Experimental comparisons with four state-of-the-art approaches show that the proposed algorithm can obtain a higher accuracy, is more robust to noise, and finds solutions which are more globally optimal. Additionally, the proposed algorithm can improve the local search ability for the optimal solutions and produces better cluster centers.

On the SAR change detection review and optimal decision

Change detection is the process of identifying differences in the state of an object or scene after the occurence of an event. In this paper, we will present several similarity measures for automatic synthetic aperture radar (SAR) change detection, which can be classified into two families: the first regroups the measures based on pixel radiometry and the second collects the detectors based on local statistics. This article details a judicious method for SAR image change detection using Dezert–Smarandache Theory (DSmT). On the one hand, a Rayleigh distribution function is used to characterize globally the radar texture data, which allows mass assignment through the Kullback–Leibler distance. On the other hand, local pixel measurements are introduced through the Rayleigh Distribution Ratio indicator to refine the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. This technique has been applied on both simulated and real data and allows very satisfactory change detection results.

Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm

Change detection in synthetic aperture radar (SAR) images has become increasingly important along with the development of SAR techniques. In this study, a novel locally fitting and expectation-maximization (EM) approach is proposed for unsupervised change detection tasks in SAR images. A difference image is generated first, and an in-depth study of the inherent characteristics of the histogram of the difference image is then made. Thus, the new approach is proposed corresponding to these characteristics. In this approach, the locally fitting model orientated to deal with the unchanged class is put forward to reach a best fit, and the semi-EM algorithm for the changed class is used to tackle the phenomenon of overlapping. Then, through the Bayesian decision rule, the optimal threshold is determined. Our contributions lie in two aspects. First, in the locally fitting model, the location of the optimal threshold can be determined, which leads to an accurate fit over a short interval. Second, the use of the semi-EM algorithm not only retains the efficacy of the EM algorithm to cope with overlapping for the changed class but also simplifies the computing process. Experiments on real data sets confirm the effectiveness of the proposed approach, which results in final maps very similar to the ground truth and is more effective in determining the optimal threshold in comparison with others. The experimental results also demonstrate its effectiveness when the changed areas are of different geometrical shapes.