Using Fuzzy Logic to Enhance the Large Size Remote Sensing Images

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1 Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 Usng Fuzzy Logc to Enhance the Large Sze Remote Sensng Images Trung Nguyen Tu Huy Ngo Hoang and Thoa Vu Van Abstract The mage enhancement methods based on fuzzy clusterng make mage whch qualty clearly hgher the tradtonal methods. However actually the methods have problems wth the large sze remote sensng that s the nherent dsadvantage of the fuzzy clusterng algorthms. In addton the methods only work on each band therefore the color of mage s not conserved after enhancement. Ths paper presents a new algorthm of mage enhancement wth combnaton of two fuzzy clusterng levels and multple bands grey level adjust model whch can surmount the above dsadvantages. Index Terms Image enhancement fuzzfcaton defuzzfcaton grey level adjust. I. INTRODUCTION Remote sensng mages often have large sze and hgh resoluton. They can also contan noses. For nose reducton and mage enhancement we need to use the mage qualty enhancng methods. Qualty Enhancement ncludes two separate phases: Image Enhancement and Restore. Noses n remote sensng mages nclude common noses lke color mages and specfc noses as mst cloud For specfc noses we need specfc methods of nose reducton such as removng cloud and mst usng Mallat algorthm [1]. For common noses we can use the common nose reducton methods such as nose flter mage smoothng; contrast enhancement adjustng grey levels of mages; Many common contrast enhancement methods apply the global approach to enhance all brghtness levels of mages. Ths can loss the local contrast nformaton and detals n brght and dark regons. In [] the authors combne the fuzzy clusterng and the grey level adjustng formulas to enhance contrast of medcal mages. However n realty the methods have problems wth the large sze remote sensng mages that s the nherent dsadvantage of the fuzzy clusterng algorthms. In addton the methods only process each band. In [] the medcal mages are the grey mages. So f the methods apply to multple band mages such as color mages the color of mage s not often conserved after enhancement. Ths paper presents a new algorthm of mage enhancement based on fuzzy clusterng whch can perform wth the large sze remote sensng mages and process smultaneously multple bands combnng auto thresholds computng followng each cluster. Remanng sectons of ths paper are presented as follow. Manuscrpt receved December 014; revsed March Trung Nguyen Tu and Thoa Vu Van are wth Post & Telecommuncatons Insttute of Technology Vetnam (e-mal: {trungnt.srems thoa36}@gmal.com). Huy Ngo Hoang s wth Insttute of Informaton Technology Vetnamese Academy of Scence and Technology Vetnam (e-mal: nhhuy@ot.ac.vn). Secton II presents the algorthm of enhancng the large sze remote sensng mages based on fuzzy technque. Experments are presented n Secton III. Secton IV s the concluson of the paper. II. ENHANCING THE LARGE SIZE REMOTE SENSING IMAGES BASE ON FUZZY TECHNIQUE My fuzzy mage enhancement algorthm s mplemented wth three stages as n Table I. Stage Stage 1 Stage Stage 3 TABLE I: ENHANCING IMAGE WITH ALGORITHM Task Fuzzfcaton wth lsfcm Creatng the grey level adjust model Defuzzfcaton wth the grey level adjust model A. Fuzzfcaton wth lsfcm 1) FCM clusterng algorthm Fuzzy c-means clusterng algorthm [3] of fuzzy segmentaton s wdely used. Whle consderng fuzzy logc set the algorthm s developed based on k-means clusterng algorthm. In ths algorthm each pxel does not only belong to any cluster and represented by multple membershp of each cluster. Clusterng algorthm s performed wth teratng optmzaton of mnmzng fuzzy objectve functon (J m ) whch s defned as equaton 6 ([4] [5]). c: number of clusters. n: number of pxels of mage. µ k : membershp value of th cluster of kth pxel m: fuzzy parameter x k : vector of kth pxel V : center vector of th cluster d (x k V ): Eucldean dstance between x k and V Membershp(µ k ) s estmated wth dstance between x k and V and bounded as followng: Center of cluster V and the member matrx μ k (of the member matrx μ) can be computed by the formula: (1) () 1 c (3) 1 c 1 k n (4) DOI: /IJIEE.015.V

2 Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 Therefore J m can be mnmzed by teratng through equatons (3) and (4). The frst step of teratng s ntalzng fxed cluster number c fuzzy parameter m convergence threshold ε then computng μ k and V usng equatons (3) and (4). Iteratng s fnshed when the change of V between two teratons s smaller than ε. Fnally each s classfed nto a combnaton of membershps of clusters. ) Remote sensng mage clusterng wth lsfcm The fuzzy enhancng methods only process each band. In [] the medcal mages are the grey mages. Inherently FCM algorthm [3] can perform clusterng wth multple bands. Actually ths algorthm s used effectvely for color mage clusterng. Therefore we mprove some grey level adjust functons (see secton.b) to use ths utlty for clusterng multple bands of remote sensng mages. A dfferent problem needs to be solved. The FCM algorthm [3] cannot perform for large sze mages such as remote sensng mages. FCM algorthm process to cluster drectly nput mage. It s not sure ths can be executed wth large sze remote sensng mages. The fuzzy clusterng has problem when beng executng large sze mages whch are remote sensng mages wth hgh resoluton. The problem s due the member matrx. Accordng to formula 4 (secton.a.1) sze of µ s computed as followng: (Byte) (5) where s cluster count s pxel count of mage. Supposng we have a mage wth sze we need segment to 0 clusters. Then s. Ths matrx s stored n RAM. In ths case we need RAM 1GB to store the matrx. However f we want to segment to 40 clusters s. It means f we have only RAM 1GB then t wll not contan enough elements of the matrx. Then to execute FCM algorthm we must ncrease RAM. If the sze of mage s cluster count = 0 Sze µ s. Wth the above mage sze and the above cluster count even wth largest sze RAM at the moment PC (personal computer) can not also contan the matrx whch leads to the fact that FCM unable to execute f ths matrx s stored n RAM. Therefore we can thnk of usng hard dsk to store ths. However then even wth the common color mages executng tme of FCM s very low. Wth remote sensng ths tme can be up to day unt. Therefore t s not effectve. The above analyss explans the reason why FCM [3] has problems wth large sze mages that are specfc remote sensng mages. If FCM algorthm [3] for large sze remote sensng mages s mproved mages enhancement algorthm wll solve the above problem. Therefore we propose algorthm of fuzzy clusterng whch we call lsfcm (large sze mage Fuzzy cmeans) for large sze remote sensng mages. Algorthm dagram s descrbed n Fg. 1. Step 1: Dvde cells We can descrbe mages under pxels set. Supposng mage has sze. Then we have: Orgnal Image s dvded to (6) cells each cell has a sze not exceedng 104 x 104 ( ) pxels to assure that FCM procedure can be executed. Horzontally two consecutve cells have stacked a part as large as a half of each cell. Vertcally two consecutve cells have stacked a part as large as a half of each cell. Then we have a new descrpton of mages as followng: Hence we have a set of cells. Rewrtng (6) n the one drecton form of cells we have a representaton of mage followng: where s equvalent to n (7) and. Fg. 1. Dagram of lsfcm algorthm. Step : FCM clusterng for each cell Perform clusterng each cell wth FCM algorthm. After all cells are clustered we obtan the center set as followng: (7) (8) (9) (10) Step 3: FCM clusterng for cluster centers set After collectng cluster centers set of all cells we perform FCM clusterng on ths set. Then we wll cluster centers set wth c fnal centers. Step 4: Synthess From c cluster centers whch are obtaned n Step 3 recalculate membershp values of each pxel n orgnal mage for each cluster center. B. Creatng the Grey Level Adjust Model In ths stage we wll construct the grey level adjust functon to enhance each cluster. Ths functon s created from grey level stretch fomula followng: (11) 465

3 Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 The grey level adjust functon s formulated followng: (1) where s mínmum value s maxmum value s lower boundary s upper boundary s old grey level value s new grey level value. Then the thresholds and s computed follow each band and each cluster. Suppose s called the dstrbuted functon of the grey level followng each cluster. and the parameters: s showed n Fg.. g: orgnal gray value. b u : upper boundary for stretchng of th cluster. b l : lower boundary for stretchng of th cluster. V : center of th cluster. C. Defuzzfcaton wth the Mult Bands Grey Level Adjust Model Defuzzfcaton s process of mage transformaton from membershp space back to the gray level space. Based on the grey level adjust model for each bult n secton. grey values of each pxel are enhanced to new values. Ths enhancng functon has a general form as followng: g g = T(g). Where (13) Fg.. Dstrbuted functon and thresholds. The thresholds and s determned by selectng so that area of subregon betwean and values equalze 95% area of regon buldt by graph of the dstrbuted functon. Table II lst the most of the grey adjust fomulas n []. TABLE II: THE GREY LEVEL ADJUST FOMULAS IN [] Meanng Adjust formulas T (g)/ ntermedate parameters Possblty Dstrbuton Intensfcaton Operator Hyperbolzaton g b l b l V g * * bl g V b l g V b l V g * 1 g V V b l g V b u V g * 1 V g b u V g V b V g * * b u V u 1.5 b u Fd * ' Where: b u V Fd bu g 1 Fd ' * ' 1 * ( g ) e 1 1 e 1 Where: 0 g bl g bl b l g b b u b l 1 bu g 55 u g b u The enhancng algorthms only bult grey level adjust functons for a band []. So fuzzfcaton also only performs on each band (see secton.1). Assumng each pxel P has 3 grey values correspondng 3 bands s Fuzzfcaton whch s performed ndependently on each band leadng to wll have correspondng 3 sets membershp values wth sets of cluster on each band that sn t related together. Ths leads to the loss of the relatonshp of 3 grey values n the same pxel. Then new values after transformaton also lose ths feature. So the color of enhanced mage s not conserved. However f we perform smultaneously fuzzfcaton on multple bands each pxel or has the same set of membershp values wth same set of clusters. Ths means the relaton of grey values n the same pxel does not break. Based on formula (13) and applyng smultaneously FCM for multple bands (see secton A.1) we modfy grey level functon for multple bands and the functon wth 3 bands stated as followng: where = (14) (15) s old grey values of pxel P and new grey values of pxel P. III. EXPERIMENTS We performed two experments. The frst experment s performed wth a sample whch s small sze mage. The second experment s performed wth a sample whch s large sze mage. In both experments enhanced mage s compared wth the hstogram equalzed mage based on Entropy Index. A. Exprment 1 Input mage s a remote sensng mage whch have low contrast. Table III lst nput mage hstogram equalzed mage and enhanced mage of our mproved method. Vsually we can see the enhanced mage of mproved method s more hghlght than the stretched mage and the hstogram equalzed mage. 466

4 Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 TABLE III: ENHANCED IMAGE BY THE METHODS Meanng Image orgnal mage and mage whch s resulted by hstogram equalzaton algorthm. Orgnal mage TABLE V: ENHANCED IMAGE BY THE METHODS Meanng Image Orgnal mage Hstogram equalzaton Hstogram equalzaton Grey level stretch Grey level stretch Enhanced mage by Improved algorthm Enhanced mage by Improved algorthm In Table IV and Fg. 3 we see Entropy of mage whch s resulted by our mprovng algorthm hgher than Entropy of orgnal mage and mage whch s resulted by hstogram equalzaton algorthm TABLE IV: COMPARE ENTROPY OF THE METHODS Band Hs Eq Stretch Enh Fg. 3. Comparson entropy of each band of remote sensng mage. B. Experment 1 3 Hs Eq Stretch Enh Input mage s a large sze mage whch have low contrast ts sze s Table V lsts nput mage hstogram equalzed mage and enhanced mage of our mproved method. Vsually we can see the enhanced mage of mproved method s more hghlght than the stretched mage and the hstogram equalzed mage. In addton color of hstogram equalzed mage s changed. In Table VI and Fg. 4 we see Entropy of mage whch s resulted by our mprovng algorthm hgher than Entropy of TABLE VI: COMPARE ENTROPY OF THE METHODS Band Hs Eq Stretch Enh Fg. 4. Comparson entropy of each band of large sze remote sensng mage. IV. CONCLUSION Hs Eq Stretch Enh In ths research we propose a new algorthm for large sze remote sensng mage enhancement. Frstly mage s fuzzfcated by fuzzy clusterng for large sze mage lsfcm whch we proposed. Then mage s enhanced followng fuzzy clusters and performed smultaneously on multple bands. Experment Results show that the mproved method makes qualty of enhanced mage hgher than qualty of hstogram equalzaton and lnear stretch and performs well for large sze remote sensng mages. In the next work we are gong to parallel lsfcm algorthm based on a model of parallel computaton to ncrease processng speed of ths algorthm. 467

5 Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 REFERENCES [1] X. F. Zhu and F. Wu An mproved approach to remove cloud and mst from remote sensng mages based on mallat algorthm presented Internatonal Symposum on Photoelectronc Detecton and Imagng 007 Bejng 007. [] A. E. Hasanen and A. Badr A comparatve study on dgtal mamography enhancement algorthms based on fuzzy theory Studes n Informatcs and Control vol. 1 no. 1 March 003. [3] J. C. Bezdek R. Ehrlch and W. Full FCM: The fuzzy c-means clusterng algorthm Computers & Geoscences vol. 10 no. -3 pp [4] J. C. Bezdek Pattern Recognton wth Fuzzy Objectve Functon Algorthm New York: Plenum Press [5] T. J. Ross Fuzzy Logc wth Engneerng Applcatons Fuzzy Classfyng Hoboken NJ: John Wley pp Huy Ngo Hoang was born n 1969 at Hano. Huy graduated from Hano Pedagogcal Unversty n He s a doctoral canddate at Insttute of Informaton Technology Vetnamese Academy of Scence and Technology. He works at Insttute of Informaton Technology Vetnamese Academy of Scence and Technology. Hs research nterests are mage processng speech processng nformaton systems embedded systems automatc control. Thoa Vu Van was born n 1955 at Nnh Bnh. He graduated from Vnh Pedagogcal Unversty n He receved a PhD degree n He works at Post & Telecommuncatons Insttute of Technology. Hs research nterests are algormth theory optmzaton geographc nformaton system network. systems. Trung Nguyen Tu was born n 1985 at Hano. Trung graduated from Hano Pedagogcal Unversty N o n 007 and completed hs masters from Unversty of Engneerng and Technology n 011. Trung s a doctoral canddate at Post & Telecommuncatons Insttute of Technology. Hs research nterests are mage processng speech processng nformaton systems embedded 468

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