SAR Image Change Detection Research Articles

Research of parallel evolutionary algorithms and its application in image processing

Evolutionary computation is the cross-cutting and frontier research field of information science, automation science and computer science. It is also the research direction of computational intelligence. In this paper, based on the evolutionary computation theory, a series model of evolutionary computation is established and the convergence of typical evolutionary algorithms is analyzed. The adaptive evolutionary algorithm based on population information entropy and its application in image is proposed. The paper proposes a decision-making algorithm based on bit-coded discernible matrix and its application in image analysis. The idea of parallel computing is applied to the problem of SAR image change detection and image feature extraction. The corresponding parallel computing framework is designed to improve the running speed of the algorithm. Experimental results showed that the proposed algorithm can improve the accuracy and processing speed of the algorithm, which satisfies the practical application requirements.

Discriminative Random Fields Based on Maximum Entropy Principle for Semisupervised SAR Image Change Detection

This paper proposes a novel semisupervised SAR images change detection algorithm using discriminative random fields based on maximum entropy principle (MEDRF). MEDRF is a discriminative model fused by two generative models, named as the bias model and the correction model, based on maximum entropy (ME) principle. In MEDRF model, we construct the bias model and the correction model on labeled samples and unlabeled samples, respectively, based on Markov random fields (MRF) to capture the multitemporal image information. Then, we deduce two constraints from the two generative models, and thus fuse the bias model and the correction model to derive MEDRF model according to ME principle subjected to the two constraints. In this way, the proposed MEDRF takes full advantages of the image information from the labeled samples and the unlabeled samples, especially including the spatial-contextual information, to provide an appropriate class boundary. In the experiment, we analyze the influence of the number of labeled samples to the performance of MEDRF model in semisupervised change detection to illustrate that MEDRF can achieve appropriate detection results even using a small number of labeled samples, and the experimental results on real SAR data demonstrate MEDRF model is able to achieve improvement in change detection over several methods proposed recently.

SAR Image Change Detection Method Based on Pulse-Coupled Neural Network

The study proposes a new algorithm for change detection of SAR images based on segmentation to improve the accuracy of the SAR image change detection. The ratio method is used to acquire the difference image (DI). Then, the global dictionary is applied to address the image denoising problem. Finally, change mask is obtained by pulse-coupled neural network (PCNN). The results of the experiment show that the proposed method improves accuracy.

Gabor Feature Based Unsupervised Change Detection of Multitemporal SAR Images Based on Two-Level Clustering

In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

Change Detection of High Resolution SAR Images by the Fusion of Coherent/Incoherent Information

Change Detection of High Resolution SAR Images by the Fusion of Coherent/Incoherent Information

Applying Image Fusion Technique with MRFKMC for Change Detection in SAR Images

Applying Image Fusion Technique with MRFKMC for Change Detection in SAR Images

Change Detection in SAR Images Based on Semi-Supervised Learning

Support Vector Machine (SVM) is a supervised approach, which needs large numbers of labeled samples. However, it is difficult to obtain such samples for change detection based on SAR images and the available labeled samples are very limited. this paper proposes a semi-supervised support vector machine (S3VM) unsupervised SAR image change detection. Using of K-means clustering method obtain threshold of image; introduce offsets which are automatically selected to achieve a pseudo-training set and unlabeled set; Finally, based on the statistical characteristics of semi-supervised support vector machines for image change and non-change class. The experimental results showed that: In the case without using noise reduction and the reduction in the number of samples, the proposed algorithm can maintain better classification, generalization and more stable detection accuracy.

Fast SAR image change detection using Bayesian approach based difference image and modified statistical region merging.

A novel fast SAR image change detection method is presented in this paper. Based on a Bayesian approach, the prior information that speckles follow the Nakagami distribution is incorporated into the difference image (DI) generation process. The new DI performs much better than the familiar log ratio (LR) DI as well as the cumulant based Kullback-Leibler divergence (CKLD) DI. The statistical region merging (SRM) approach is first introduced to change detection context. A new clustering procedure with the region variance as the statistical inference variable is exhibited to tailor SAR image change detection purposes, with only two classes in the final map, the unchanged and changed classes. The most prominent advantages of the proposed modified SRM (MSRM) method are the ability to cope with noise corruption and the quick implementation. Experimental results show that the proposed method is superior in both the change detection accuracy and the operation efficiency.

SAR image change detection based on Memetic algorithm

This paper proposed an unsupervised technique for detecting changed areas between multitemporal SAR images.Different with the original ones,the clustering method was used here to find the change map by minimizing mean square error with evolution algorithm.After introducing the image character,a new search strategy in Memetic algorithm was given here,which adjusted the local search algorithm according to the current detection result.The approach was distribution free and did not need priori knowledge.The experimental results obtained on the real SAR images showed that the proposed method had a higher convergence speed than GA,ICSA and original MA,the detection results demonstrated the effectiveness of the proposed algorithm.

Change detection of SAR images based on wavelet domain Fisher classifier

An unsupervised technique for detecting change area between two SAR images was proposed.The detection process is based on distribution property of the joint intensity histograms and need not distribution hypothesis.The algorithm uses adaptive edge detection to get training data.The joint intensity histograms in different levels are used to decide the membership degree of unlabeled points through Fisher classifier.The fusion model which considers the context relationship and inter-scale information improves the sensitivity.The simulation results of two real SAR images show that the algorithm is effective and has better detection results.

A SAR Image Change Detection Algorithm Based on Principal Component Analysis

摘要: 该文提出一种基于主分量分析(PCA)的SAR图像变化检测算法。该算法将SAR图像转化为列向量,对两个图像向量组成的矩阵进行主分量分解,其图像的变化部分则表征为矩阵的次分量成分。论文还研究了高效的SAR图像变化检测实现算法,最后通过与对数比方法和分块主分量分析法进行比较实验,实验结果证实了方法的有效性。 关键词: SAR图像; 变化检测; 图像向量化; 主分量分析

Change detection in SAR images by means of grouping connected regions using clone selection algorithm

Most of the change detection techniques for SAR images based on the analysis of the difference image always generate the result map at the pixel level, which leads to a noisy change detection map, with holes in connected components and jagged boundaries. To attack this problem, a novel technique to generate the result at the region level is proposed, which considers each connected region as an agent and then gives the optimal label combination for all agents through the clone selection algorithm and a new fitness function. The region oriented method can avoid the above problem and improve the change detection performance. Experimental results on the real Radarsat SAR dataset validate the effectiveness of the method on both the quantitative and subjective aspects.

Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications

Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data have been acquired by different instruments. For this reason, it is interesting to study those indicators that do not rely completely on radiometric values. In this work a new approach is proposed based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to establish the limits of their applicability and their sensitivity to the occurred changes. Initial results are promising and suggest similarity measures as possiblechange detectors in multi-sensor configurations.

Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis

This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.

Unsupervised change detection in multitemporal SAR images using MRF models

An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.

Automatic Identification of the Number and Values of Decision Thresholds in the Log-Ratio Image for Change Detection in SAR Images

In this letter, we propose an extension of an automatic and unsupervised change-detection method for synthetic aperture radar images we presented earlier. By analyzing a properly defined cost function, the proposed method allows the automatic detection of the number (zero, one, or two) and the values of the decision thresholds associated with changes (if any) in the log-ratio image. This cost function is the minimum value of the criterion function adopted to select the decision threshold in the log-ratio image according to a modified double-thresholding Kittler-Illingworth algorithm (implemented under the generalized Gaussian assumption for changed and unchanged classes). Experimental results carried out both on real and simulated multitemporal synthetic aperture radar images proved the effectiveness of the proposed automatic method in detecting both the number of changes to be identified (the situation of no changes is also explicitly identified) and the related threshold values

Automatic analysis of change detection of multi-temporal ERS-2 SAR images by using two-threshold EM and MRF algorithms*

Abstract To automatically detect and analyze the surface change in the urban area from multi-temporal SAR images, an algorithm of two-threshold expectation maximum (EM) and Markov random field (MRF) is developed. Difference of the SAR images demonstrates variation of backscattering caused by the surface change all over the image pixels. Two thresholds are obtained by the EM iterative process and categorized to three classes: enhanced scattering, reduced scattering and unchanged regimes. Initializing from the EM result, the iterated conditional modes (ICM) algorithm of the MRF is then used to analyze the detection of contexture change in the urban area. As an example, two images of the ERS-2 SAR in 1996 and 2002 over the Shanghai City are studied.

Change detection in SAR imagery

Abstract The two major difficulties associated with SAR image change detection are addressed. These are the removal of speckle noise and the registration of information between images. The problem of image registration is severe in airborne SAR imagery due to the unpredictable nature of the aircraft track. Although inertial navigation systems may be employed to measure this motion, the accuracy obtained is insufficient to allow the creation of large rectilinear images. However, autofocus techniques are used here to measure residual aircraft motions thus allowing the production of large geometrically accurate images. The second problem of speckle reduction has been approached in two ways. The first technique applies an intensity segmentation algorithm to each image and the regions generated by this segmentation are then compared using the change detection algorithm. An alternative approach to the problem of speckle removal is to use neural network methods to learn the speckle removal and region generation ...