COMPARATIVE ALGORITHMS FOR AUTOMATIC DETECTION OF OIL SPILL INMULTISAR OF RADARSAT-1 SAR AND ENVISAT DATA

Maged Marghany and Mazlan HashimInstitute of Geospatial Science and Technology (INSTeG),

UniversitiTeknologi Malaysia 81310 UTM, Skudai, Johor Bahru, MalaysiaEmails: maged@utm.my,

:magedupm@hotmail.com

ABSTRACTThis study presents a comparative algorithms for oil spillautomatic detection from different RADARSAT-1 SAR differentmode data and ENVISAT ASAR data. Three algorithms areinvolved: Entropy, Mahalanobis, and Artificial Neural Network(ANN) algorithms. The study shows that ANN provideautomatically oil spill detection with error of standard deviation of0.12 which is lower than Entropy and the Mahalanobis algorithms.

Index Terms— RADARSAT-1 SAR,oil spill, Entropy,Mahalanobis neural net work (NN).

1. INTRODUCTIONUnintentionally, ocean, seas and coasts are polluted bymineral oil, mainly because of tanker rupture. Illegal oildischarges by ships or natural oil seepage[1-3]. The resultingoil slicks are hard to control, as their evolution relies onweather, currents, tides, and many chemicals and physicalfactors (like the presence of icebergs). It is imperative toacquire an overall circumstances to determine its trajectorymovement[1]. The Synthetic Aperture Radar (SAR)instrument, that can assemble data autonomously of weatherand light circumstances. SAR is an excellent tool to surveyand detect oil in oceans and seas. Further, SAR offers themost means of recording oil pollution. Incidentally, oilslicks appear as dark patches on SAR images because of thedamping effect of the oil on the backscattered signals fromthe SAR instrument[3].

In this paper, we address the question of utilizationmultiSAR data for monitoring oil spill disasters from space.Indeed, there are several factors could impact the accuracyof oil spill detection such as sensor type, wind speed (> 3m/s), discrimination between oil spill, look-alikes and windshadow zone. Previous studies have used single data setwhich is not adequate to deliver any accurate decisionregarding the implementation of different texture algorithmsfor oil spill detection [8].

2. HYPOTHESES AND OBJECTIVEThis work has hypothesized that the dark spot areas (oilslick or look-alike pixels) and its surrounding backscattered

environmental signals in the SAR data can be modeled astexture. In this context, a co-occurrence texture algorithmentropy, post supervised classification, and neural network(NN) can be used as a semiautomatic tool to discriminatebetween oil spills, look-alikes and surrounding sea surfacewaters.

In doing so, this study is extended the previous work doneby Marghany and Hashim [8,9] used the RADARSAT-1SAR different beam mode data i.e., Wide beam mode (W1)and Standard beam mode (S2) to illustrate the extendedtheory.

The main objective of this work is to develop comparativeautomatic detection procedures for oil spill pixels inmultimode (Standard beam S2, Wide beam W1 and finebeam F1) RADARSAT-1 SAR satellite and ENVISATdata, using three algorithms namely, textures using co-occurrence matrix, post supervised classification, and neuralnetwork (NN) for oil spill detection with window size 7 x 7[8,9].

3. DATA ACQUISITIONIn this study, Standard beam mode (S2); Wide (W1); andFine (F1) beam mode data from RADARSAT-1 SAR andand ENVISAT ASAR data are used. RADARSAT-1 SARacquired along the Malacca Straits coastal waters, Malaysia.In contrary, ENVISAT ASAR data in the Gulf of Mexico onMay 9 2010. Consistent with Marghany [1,3], Marghany andHashim [9] oil spill occurred on 17 December 1999, alongthe coastal water of Malacca Straits.

4. METHODSIn this study, three algorithms are implemented: Co-occurrence textures[1]; post supervised classification[2]; andneural net work (NN)[3,5-6] for oil spill automatic detectionin SAR data. The co-occurrence textures involved Entropyalgorithm [1]. Entropy is implemented to the different

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RADARSAT-1 SAR mode data with window Kernel size of7x7 pixels and lines. Additionally, the post supervisedclassification is implemented to RADARSAT-1 SAR datausing Mahalanobis classifier[3]. Finally, Artificial NeuralNet work technique (ANN) is also implemented to SAR datausing back propagation algorithm[6].

4.1. Entropy algorithmFollowing Marghany and Hashim [8], co-occurrence isapplied to categorize the image to “oil slick” and water.Entropy texture with 0° angular relationship and d=1 isemployed. On other hand, RADARSAT-1 SAR gray tonecan describe as texture (i.e., the microstructure).Mathematically, a co-occurrence matrix C is defined over ann x m SAR data intensity I, parameterized by an offset(Δx,Δy), as:

, ( , )1 1

1, if ( , ) and ( , )

0, otherwise

n m

x y i jp q

I p q i I p x q y jC

(1)

According to Marghany [1], the texture feature for oil spilland look-alike detections are computed by the followingformula:

logij ijEnt p p (2)where Ent is Entropy, i and j are the row and column, pxi andpyj are the marginal probability matrix obtained through thesummation of pijin the direction of the row and column. Inthis study, the window size is 7x7 and 3x3 pixels and lines[1].

4.2. Mahalanobis ClassificationThe input parameters from SAR data can impact theMahalanobis classifier running when the variability innerclasses is smaller than whole classifier group variability. Inthis context, if the classes M are badly scaled and thedecision boundaries between classes are curved, theclassifier accuracy is reduced. Formally, the Mahalanobisdistant of a multivariate vector is given as[9]:

( )

1( ) ( )v

TMD v S v (3)

where 1 2 3( , , ............, )tnV v v v v from group of values

with mean tn )..,..........,,( 321 , and S, is

covariance matrix. In order to apply Mahalanobisclassification procedures to different remote sensing data, letv be the feature vector for the unknown input, and let M1,M2 be the two classes: oil spill, and look-alike [2]. Then theerror in matching v against Mj is given by [v- Mj], theEuclidean distance. Further description of Mahalanobisclassifier can find in Marghany and Hashim [9].

4.3. Artificial Neural Network (ANN)The major steps in the training algorithm are: Feed forwardcalculations, propagating error from output layer to inputlayer and weight updating in hidden and output layers [5].

Forward pass phase calculations are shown by the followingequations between input (i) and hidden (j) [3], [6]. For thisapplication, a multi-layer feed forward network with errorback-propagation has been employed [3],[5]. FollowingTopouzelis et al.[3], the ANN’s and the pattern recognition(PR) technique, feed forward network with back-propagation algorithm are used in this study.

( )1

1 j ij i jj we

. (4)

( )1

1 k jk j kk we

. (5)

where j is the output of node j, i is the output of node i,

k is the output of node jkw is the weight connected

between node i and j, and j is the bias of node j, k is thebias of node k. In backward pass phase, error propagatedbackward through the network from output layer to inputlayer as represented in equation (4). Following Topouzelis etal. [6]. The weights are modified to minimize mean squareerror (MSE) [9]. Finally, error standard deviation is used todetermine the accuracy level of each algorithm has beenused in this study.

21

1 ( )n mi j a ij ijMSE d y

n (6)

where ijd is the thj desired output for the thi training

pattern, and ijy is the corresponding actual output.

5. RESULTS AND DISCUSSIONFig. 1 shows the radar cross section intensity along oil spillin different SAR mode data. The backscattered intensity isdamped by –8 dB to -18 dB in W1, -10dB to -18 dB in S2and -11dB to -18 dB in F1 mode data. Further, threedifferent mode backscatter intensities are above theRADARSAT-1 noise floor value of nominally –20 dB(Fig.1).

Fig.1. Radar cross section intensity along oil spill indifferent SAR mode data.

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Fig. 2 shows the clear oil spill morphology signature thatextracted using Entropy algorithm. Indeed, entropy measuresthe absolute variability in backscatter change over theselected window. This result confirms the studies of andMarghany [1] Marghany and Hashim [8].

Fig.2. Entropy algorithm for different RADARSAT-1SAR mode (a) Wide mode (W1), (b) Standard mode (S2)and (c) Fine mode (F1) Data.

Figs. 3 and 4 show the results of Mahalanobis classificationand Artificial Neural Network (ANN), respectively. Clearly,that neural network algorithm is able to isolate oil spill darkpixels from the surrounding environment. On other hands,look-alikes, low wind zone, sea surface roughness, and landare marked by white colour while oil spill pixels are markedall black.

Fig. 3. Mahalanobis Classifier (a) Wide mode (W1), (b)Standard mode (S2) and (c) Fine mode (F1) Data.

Fig. (4b) does not show any class presence or existence ofoil spill event. Further, Fig. 4 shows the results of theArtificial Neural Net work, where 99% of the oil spills in thetest set were correctly classified that using multilayerperceptron (MLP) neural network with two hidden layers.The net is trained using the back-propagation algorithm tominimize the error function. 99% of oil spills areautomatically. This study agrees with study of Topouzelis etal., [3,5].

Fig. 4. Neural Network for Automatic Detection of Oil Spillfrom (a) W1, (b) S2, and (c) F1 Mode Data.

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Fig. 5 shows further comparison between differentalgorithms in ENVISAT ASAR data that acquired during thedisaster of the Deepwater Horizon oil spill in the Gulf ofMexico, May 9 2010. Fig. 5 confirm the results of Figs.2 to4.

Fig.5 Different algorithms with ENVISAT data.

Finally Fig. 6 shows that confirms that ANN algorithmperformance better than other algorithms with standard errorof 0.12. Indeed, a network is examined to assess the relativeimportance of its weights, and the least important ones aredeleted. Typically, this is followed by some further trainingof the pruned network, and the procedure of pruning andtraining may be repeated for several cycles[3,5,6,9].

Fig.6. Error standard deviation of different algorithms.

6. CONCLUSIONSThis study has demonstrated a comparative algorithms foroil spill automatic detection from different RADARSAT-1SAR different mode data and ENVISAT ASAR data. Threealgorithms are involved: Entropy, Mahalanobis, andArtificial Neural Network (ANN) algorithms. The studyshows that ANN provide automatically oil spill detection

with error of standard deviation of 0.12 which is lower thanEntropy and the Mahalanobis algorithms.

6. REFERENCES

[1] M. Marghany, “RADARSAT Automatic Algorithms forDetecting Coastal Oil Spill Pollution”. Int. J. of App. Earth Obs.and Geo. 3, 191-196, 2001.[2] B. Fiscella , A. Giancaspro, F. Nirchio, P. Pavese, and P.Trivero, P, Oil Spill Detection Using Marine SAR Images”. Int. J.of Remote Sens. 21,3561-3566, 2000.[3] K.Topouzelis, V. Karathanassi,P. Pavlakis, and D. Rokos,“Potentiality of Feed-Forward Neural Networks for ClassifyingDark Formations to Oil Spills and Look-alikes”. Geo. Int. 24, 179-19, 2009.[4] M. Marghany, A.P. Cracknell, and M. Hashim, “ Modificationof Fractal Algorithm for Oil Spill Detection from RADARSAT-1SAR Data”. Int. J. of App. Earth Obs. and Geo. 11,96-102, 2009.[5] K.Topouzelis, V. Karathanassi, P. Pavlakis, and D. Rokos,“Detection and Discrimination between Oil Spills and Look-alikePhenomena through Neural Networks. ISPRS J. Photo. Remote.Sens. 62, 264-270, 2007.[6] K.Topouzelis, “Oil Spill Detection by SAR Images: DarkFormation detection, Feature Extraction and ClassificationAlgorithms”. Sens. 8, 6642-6659,2008.[7] M. Marghany, A.P.Cracknell, and M. Hashim, “Comparisonbetween Radarsat-1 SAR Different Data Modes for Oil SpillDetection by a Fractal Box Counting Algorithm”. Int. J. of Dig.Earth, 2, 237-256, 2009.[8] M. Marghany, and M. Hashim, “Texture entropy algorithm forautomatic detection of oil spill from RADARSAT-1 SAR data”.Int. J. of the Phys. Sci. 5(9), pp. 1475-1480, 2010.[9] M. Marghany, and M. Hashim, “Comparison betweenMahalanobis classification and neural network for oil spilldetection using RADARSAT-1 SAR data”. Int. J. of the Phys. Sci.Vol. 6(3), pp. 566-576, 2011.

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