analysis of sar images with various change detection techniques

International Journal of Emerging Technologies and Engineering (IJETE)Volume 1 Issue 2 March 2014, ISSN 2348 8050

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Analysis of SAR Images with various Change Detection TechniquesR. Vijayalakshmi, K. Madhan Kumar

PG Scholar, Department of ECE, PET Engineering College, IndiaProfessor, Department of ECE, PET Engineering College, India

ABSTRACTImage change detection is a process that analyzes imagesof the same scene taken at different times in order toidentify changes that may have occurred between theconsidered acquisition dates. It has attracted widespreadinterest in the last decades, due to a large number ofapplications in diverse disciplines such as remotesensing, medical diagnosis and video surveillance. Withthe development of remote sensing technology, changedetection in remote sensing images becomes more andmore important. Among them, change detection insynthetic aperture radar (SAR) images exhibits somemore difficulties than optical ones due to the fact thatSAR images suffer from the presence of the specklenoise. However, SAR sensors are independent ofatmospheric and sunlight conditions, which make thechange detection in SAR images still attractive. Thispaper presents a survey and analysis of change detectionapproaches dealt by various techniques.

Keywords: Change detection, Difference images, Feature,FLICM, Remote Sensing.

1. INTRODUCTIONApplications in diverse disciplines, including

remote sensing, surveillance, medical diagnosis andtreatment, civil infrastructure, and underwater sensingetc., detecting regions of change in multiple images ofthe same scene taken at different times is of widespreadinterest. SAR images suffer from the presence of thespeckle noise. The SAR sensors are independent ofatmospheric and sunlight conditions. It makes thechange detection in SAR images still attractive [2]. Thepresence of speckle noise degrades SAR imagessignificantly and it hide some important details on theimages, leading to the loss of crucial information [5].

There are some change detection methods using thesame framework for synthetic aperture radar (SAR)images [7]. It achieves satisfactory results by usingcomplicated data modeling and parameter estimation.These methods are applied to the raw data domain and

suffer from the inference of speckle noise. The multitemporal SAR images are three dimensional (3-D)datasets. It has two axes corresponding to theconventional spatial domain and the third axis to thetemporal direction [2], [3].

Temporal changes correspond to physical changesthat may occur on the ground surface and can beobserved in the variations of the SAR backscatteringcoefficient [13]. Remote sensing images, differencing(subtraction operator) and rationing (ratio operator) arewell-known techniques for producing a differenceimage. In differencing, changes are measured bysubtracting the intensity values pixel by pixel betweenthe considered couple of temporal images [13]. Inrationing, changes are obtained by applying a pixel-by-pixel ratio operator to the considered couple of temporalimages. In the case of SAR images, the ratio operator istypically used instead of the subtraction operator sincethe image differencing technique is not adapted to thestatistics of SAR images and non-robust to calibrationerrors. The estimation of the parameters of the Gaussianmodel is carried out using the expectationmaximization (EM) algorithm in the hypothesis ofGaussian distribution for changed and unchanged classes[6]. SAR-image change detection is mainly relied on thequality of the difference image and the accuracy of theclassification method. For addressing the two issues,unsupervised distribution free SAR image changedetection approach is described. It is unique in thefollowing two aspects: 1) producing difference imagesby fusing a mean-ratio image and a log-ratio image, and2) improving the fuzzy local-information c-means(FLICM) clustering algorithm [15],[4], which isinsensitive to noise. It identifies the change areas in thedifference image, without any distribution assumption.

2. SURVEY ON CHANGE DETECTIONTECHNIQUE IN SAR IMAGES


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Process Diagram

Fig 1: Process diagram of change detection

2.1 Wavelet Fusion on Ratio Images for ChangeDetection in SAR Images

It presents a novel method based on waveletfusion for change detection in synthetic aperture radar(SAR) images. The proposed approach is applied togenerate the difference image (DI) by usingcomplementary information from mean-ratio and log-ratio images [18]. To restrain the background(unchanged areas) information and enhance theinformation of changed regions in the fused DI, fusionrules based on weight averaging and minimum standarddeviation are chosen to fuse the wavelet coefficients forlow- and high-frequency bands, respectively [18].Experiments on real SAR images confirm that it doesbetter than the mean-ratio, log-ratio, and Rayleigh-distribution-ratio operators. DWT (discreat wavelettransform) used for the pixel-level image fusion. DWTisolates frequencies in both time and space, allowingdetail information to be easily extracted fromimages.Compared with DWT, transforms such ascurvelet and contourlet are proved to have better shift-invariance property and directional selectivity.The fusion rules can be described as,

Where,

Wherem and l represent the mean-ratio and log-ratio images,F =new fused image.DLL= low-frequency coefficientsDLL =low-frequency coefficient.

The percentage correct classification (PCC) is given byPCC = ((TP + TN)/(TP + FP + TN + FN))

Where,TP is short for true positivesTN is short for true negatives

FN is short for false negativeThe results show that the change detection result

based on wavelet fusion can reflect the real change trendand mitigate the impact of speckle noise.

Fig 2: ROC plot comparison between the fourdetector on Bern data set

2.2 An Unsupervised Approach based on theGeneralized Gaussian Model to Automatic ChangeDetection in Multi temporal SAR Images

It present a novel automatic and unsupervisedchange-detection approach specifically oriented to theanalysis of multi temporal single-channel single-polarization synthetic aperture radar (SAR) images [6].This approach is based on a closed-loop process madeup of three main steps: 1) a novel preprocessing basedon a controlled adaptive iterative filtering; 2) acomparison between multi temporal images carried outaccording to a standard log ratio operator; and 3) a novelapproach to the automatic analysis of the log-ratio imagefor generating the change detection map [6]. The firststep aims at reducing the speckle noise in a controlledway in order to maximize the discrimination capability

Input SARimage

Featureextraction

Classificationalgorithm

Changedetection map


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between changed and unchanged classes. In the secondstep, the two filtered multi temporal images arecompared to generate a log ratio image that containsexplicit information on changed areas. The third stepproduces the change-detection map according to athresholding procedure based on a reformulation of theKittlerIllingworth (KI) threshold selection criterion.This results in a completely automatic and self consistentchange detection approach that avoids the use ofempirical methods for the selection of the best numberof filtering iterations. Problem associated with wrongnumber of iterations of the filtering process. The KIcriterion function is given by

Where,h(Xl)= histogramc(Xl ,T)= cost function. It is given by,

Where,are the posterior probabilities of the unchanged and

changed classes.

Fig. 3. Behavior of the cost function J(k) versus numberof filtering iterations (Bern dataset).

From the above figure, the graph is drawn betweenthe cost function and iterations of the filtering process.Problem associated with wrong number of iterations ofthe filtering process. After a given iteration number, theiterative filtering process may decrease the accuracy ofthe change-detection process by significantly losingdetails present in the images.

2.3 Unsupervised Change Detection in SatelliteImages Using Principal Component Analysis and k-Means Clustering

Unsupervised change detection in multitemporal satellite images using principal componentanalysis (PCA) and k-means clustering. The differenceimage is partitioned into h h non-overlapping blocks.S, S h2, orthonormal eigenvectors are extractedthrough PCA of h h non-overlapping block set tocreate an eigenvector space. Each pixel in the differenceimage is represented with an S-dimensional featurevector which is the projection of h h difference imagedata onto the generated eigenvector space. The changedetection is achieved by partitioning the feature vectorspace into two clusters using k-means clustering with k =2 and then assigning each pixel to the one of the twoclusters by using the minimum Euclidean distancebetween the pixels feature vector and mean featurevector of clusters. Effective automatic change detectionmethod is proposed by analyzing the difference image oftwo satellite images acquired from the same areacoverage but at two different time instances. The nonoverlapping blocks of the difference image are used toextract eigenvectors by applying PCA. Then, a featurevector for each pixel of the difference image is extractedby projecting its h h neighborhood data ontoeigenvector space. The feature vector space is clusteredinto two clusters using k-means algorithm [19]. Eachcluster is represented with a mean feature vector.Finally, change detection is achieved by assigning eachpixel of the difference image to the one of the clustersaccording to the minimum Euclidean distance betweenits feature vector and mean feature vector of the clusters.

An input image X of size H W and its noisyrealization X, the PSNR measured between the twoimages is calculated as

where it is assumed that the maximum intensity value ofinput images is one.The stability of the change detection algorithm againstnoise is measured by measuring the difference betweentwo change detection resultsCM1 and CM2, which arecreated as follows. Given two input images X1 and X2,the change detection algorithm is applied to generateCM1. Then, X1 is contaminated with noise, and CM2 is


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generated to find the change map between noisy X1 and X2.

Fig 4: Detection performance comparisons according to (8) against different levels of noises. (a) Zero-mean Gaussiannoise. (b) Speckle noise.

From Fig. 3 it is clear that, this algorithm is fairlyrobust against the zero-mean Gaussian and specklenoises when PSNR 20 dB, where the maximum rate ofchange is 6% for zero-mean Gaussian noise and 8% forspeckle noise with respect to no noise change detectionperformance.

2.4 Modeling SAR Images with a Generalization ofthe Rayleigh Distribution

It presents the amplitude distribution of the complexwave, the real and the imaginary components of whichare assumed to be distributed by the stable distribution,is a generalization of the Rayleigh distribution. Theamplitude distribution is a mixture of Rayleighs as isthe k-distribution in accordance with earlier work onmodeling SAR images. It shows that almost allsuccessful SAR image models could be expressed asmixture of Rayleighs. Also present parameterestimation techniques based on negative order momentsfor the new model. The performance of the model onurban images is compared with other models such asRayleigh, Weibull, and the k-distribution. Weill bull andk-distribution does not describe the data well enoughthan the Rayleigh distribution. Moreover it is sensitive tothe speckle noise.The random real vector is X=(x1,x2,.xn)T and the pdf ofwhich can be expressed in the form

Where,=meanM=covariance matrix.The class of admissible functions is defined by,

Where,r(s) =characteristic probability distribution function

The well known Rayleigh model for the amplitudedistribution is,

Generalized (heavy-tailed) rayleigh model, which isdescribed by the characteristic function as,

Where t1,t2 are the elements of the vector and=

=scale parameter.


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The performance of the k-distribution is close to that ofthe heavy-tailed Rayleigh distribution.

Fig 5: Generalized Rayleigh pdfs.

From the above figure, the parameters wereestimated to be =1.37 and =3.31. It has theparameter estimation problem for SAR histogramswhich are shifted from origin. However, care must betaken since the parameter estimation techniques haveproven to be very sensitive to the shift and in general tonoise near origin due to the use of negative ordermoments which emphasize to samples near the origin.

2.5 Change Detection in Synthetic Aperture RadarImages based on Image Fusion and Fuzzy Clustering

It presents an unsupervised distribution-free changedetection approach for synthetic aperture radar (SAR)images based on an image fusion strategy and a novelfuzzy clustering algorithm. The image fusion techniqueis introduced to generate a difference image by usingcomplementary information from a mean-ratio imageand a log-ratio image [1]. In order to restrain thebackground information and enhance the information ofchanged regions in the fused difference image, waveletfusion rules based on an average operator and minimumlocal area energy are chosen to fuse the waveletcoefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzylocal-information C-means clustering algorithm isproposed for classifying changed and unchanged regionsin the fused difference image [4]. It incorporates theinformation about spatial context in a novel fuzzy wayfor the purpose of enhancing the changed informationand of reducing the effect of speckle noise. Experimentson real SAR images show that the image fusion strategy

integrates the advantages of the log-ratio operator andthe mean-ratio operator and gains a better performance[1]. The change detection results obtained by theimproved fuzzy clustering algorithm exhibited lowererror than its preexistences, but the accuracy in detectingthe change in difference image is lesser.

The two source images used for fusion areobtained from the mean-ratio operator and the log-ratiooperator, respectively, which are commonly given by,

Xm= 1- min (1/2, 2/1)

Xl= log X2/X1= log X2 log X1Where, 1 and 2 represent the local mean values

of multi temporal SAR images X1 and X2 respectively.The fusion rules can be described as follows:

Where m and l represent the mean-ratio image andthe log-ratio image, respectively. F denotes the newfused image. DLL stands for low-frequency coefficients.

The objective function can be defined in terms of

Where,= prototype value of the kth cluster

fuzzy membership of the ith pixel with respect tocluster kN = number of the data itemsc = number of clusters

=Euclidean distance between object andthe cluster center .In addition, the calculation of the membership partition

matrix and the cluster centers is performed as follows:

This fuzzy factor can be defined mathematically asfollows:


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Dij = spatial Euclidean distance between pixels i and jV k =prototype of the center of cluster kukj = fuzzy membership of the gray value j with respect

to the kth clusterThe local coefficient of variation is defined by

where and are the intensity variance and themean in a local window of the image, respectively.

Where, the initial membership partition matrix iscomputed randomly.Finally, the RFLICM algorithm is given as follows.Step 1) set the number c of the cluster prototypes,fuzzification Parameter m and the stopping condition .Step 2) Initialize randomly the fuzzy partition matrix.Step 3) Set the loop counter b=0.Step 4) Compute the cluster prototypes usingStep 5) Calculate the fuzzy partition matrix usingStep 6) then stop; otherwise,set b=b+1, and go to step 4).

Fig 6: Multi temporal images relating to Ottawa used inthe experiments. (a)Image acquired in July 1997 during

the summer flooding. (b) Image acquired in August 1997after the summer flooding. (c) Ground truth.

Fig 7: Difference images of the Ottawa data setgenerated from (a) mean-ratio operator, (b) log-ratio

operator, and (c) wavelet fusion.

Fig 8: Change detection results of Ottawa data setachieved by (a) FCM, (b) FLICM, and (c) RFLICM.

Due to the reason of image speckle noise, thechange detection maps generated from the mean-ratiodifference image swarmed with isolated spots. FLICMincorporate both local spatial and gray information tofind a tradeoff between detail preservation and noiseremoval, but accuracy in detection is less.

3. CONCLUSION

In this paper, a brief literature survey for changedetection in SAR image is discussed elaborately. Fromthis study it is concluded that some technique is notproviding better accuracy in change detection of SARimage. Accuracy obtained for a method is about is93.8%. It is hoped that with supervised method of neuralnetwork can improve accuracy and the classification ofalgorithms into a relatively small number of categorieswill provide useful guidance to the algorithm designer.

4. ACKNOWLEDGEMENT

Apart from my effort, the success of any work dependson the support and guidelines of others. I take thisopportunity to express my gratitude to the people whohave been supported me in the successful completion ofthis work. I owe a sincere prayer to the LORDALMIGTHTY for his kind blessings without which thiswould not have been possible. I wish to take thisopportunity to express my gratitude to all who havehelped me directly or indirectly to complete this paper.

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analysis of sar images with various change detection techniques

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