Automatic Road Extraction from Satellite Image

Transcription

1 Automatic Road Extractio from Satellite Image B.Sowmya Dept. of Electroics & Cotrol Egg., Sathyabama Istitute of Sciece & Techology, Deemed Uiversity, Cheai Abstract This paper explais the task of extractig road from ay give satellite image. Road extractio plays a vital role i may areas like military, map implemetatio etc. Road is extracted from satellite image based o its spectral sigature. Sice there are more tha 16 millio colours available i ay give image ad it is difficult to aalyse the image o all of its colours, the likely colours are grouped together by image segmetatio. For that purpose fuzzy clusterig has bee used. From the segmeted image spectral iformatio of road is extracted. Spectral iformatio aloe is isufficiet as soils ad roads have the same spectral sigature. I our work, cocepts of mathematical morphology are applied to separate road from other objects havig same spectral sigature. Keywords: Image Segmetatio, Morphology, Clusterig ad Trivial Opeig 1. Itroductio It is easy to idetify roads i satellite images maually. But automatically extractig road from satellite image is a difficult task. Previous works i this area are based o statistical ad rule-based techiques [8], which depeded primarily upo spectral iformatio. As soils ad roads share the same spectral sigature it is impossible to separate them. We preset a ew method for extractig road from satellite images. It ivolves image segmetatio usig fuzzy clusterig ad feature extractio usig mathematical morphology. The most basic attribute for segmetatio is image lumiace amplitude for a moochrome image ad color compoets for a color image. Clusterig ivolves the task of dividig data poits ito homogeeous classes or clusters so that items i the same class are as similar as possible ad items i differet classes are as dissimilar as possible. I fuzzy clusterig, the data poits ca belog to more tha oe cluster, ad associated with each of the poits are membership grades, which idicate the degree to which the data poits belog to the differet clusters. Whe image segmetatio is doe usig fuzzy clusterig, the fuzzy clusterig algorithm compares the colours i a relative way ad groups them ito clusters. Aashik Hameed Dept. of Computer Sciece Egg., Aa Uiversity, Cheai Mobile: , akila @yahoo.co.i Morphological trivial opeig[1] has bee developed for the obtaiig the size iformatio of road etwork, ad to extract the road etwork from preprocessed images by removig small objects, arrow path ad oises based o the iformatio provided by the graulometry. For automatic object detectio, the criterio used i trivial opeig ca be regarded as a threshold, which should be determied from the image aalysis with respect to graulometry or the patter spectrum. The graulometry techiques ca be used to measure the size ad shape of objects i image. Opeig operatio is used to decompose a image through a series of structure elemets with a specific shape. The opeed images are compared with the origial image to geerate measures with respect to differet size of structure elemet but with same shape. The rest of the paper is orgaized as follows. Sectio II describes image segmetatio. Fuzzy clusterig algorithm used is also discussed here. Sectio III deals with the steps ivolved i Mathematical Morphology. I sectio IV the software desig is explaied. I Sectio V cocludig remarks are give. 2. Image Segmetatio Image segmetatio is a essetial prelimiary step i most automatic pictorial patter recogitio ad scee aalysis problems. Segmetatio of a image etails the divisio or separatio of the image ito regios of similar attribute [2]. The level to which this subdivisio is carried depeds o the problem beig solved. That is segmetatio should stop whe the objects of iterest i a applicatio have bee isolated. Fuzzy clusterig, which combies fuzzy logic ad cluster aalysis techiques, has experieced a spur of iterest i recet years owig to its importat applicatios i image segmetatio. I o-fuzzy or hard clusterig, data is divided ito crisp clusters, where each data poit belogs to exactly oe cluster. I fuzzy clusterig, a sigle poit ca have partial membership i more tha oe cluster Fuzzy clusterig belogs to the group of soft computig techiques (which iclude eural ets, fuzzy systems, ad geetic algorithms). I real applicatios there is very ofte o sharp boudary betwee clusters so that fuzzy clusterig is ofte better suited for the data. Membership degrees betwee zero

2 ad oe are used i fuzzy clusterig [3] istead of crisp assigmets of the data to clusters. The resultig data partitio improves data uderstadig ad reveals its iteral structure. Partitio clusterig algorithms divide up a data set ito clusters or classes, where similar data objects are assiged to the same cluster whereas dissimilar data objects should belog to differet clusters. There are may clusterig algorithms like, Hard C meas, fuzzy C meas, possibilistic fuzzy C meas, Probabilistic fuzzy C meas etc. I our work, we have used Possibilistic fuzzy C meas algorithm. This algorithm gives better reproductio of the origial image tha the fuzzy C meas algorithm [8], as a possibilstic costrait is itroduced. i.e. I this case, the PSNR value of the reproduced image is higher. 2.1 Possibilistic Fuzzy c Meas I the possibilistic approach to clusterig the membership fuctio or the degree of typicality of a poit i a fuzzy set (or cluster) is assumed to be absolute [5]. I other words, the degree of typicality does ot deped o the membership values of the same poit i other clusters cotaied i the problem domai. By cotrast, may clusterig approaches impose a probabilistic costrait, accordig to which the sum of the membership values of a poit i all the clusters must be equal to oe. As a cosequece, HCM, FCM[7] ad may other clusterig methods assumig the probabilistic costrait caot geerate membership fuctios whose values ca be iterpreted as degrees of typicality. PFCM algorithm avoids the assumptio of the probabilistic costrait. The PFCM is based o the relaxatio of the probabilistic costrait i order to iterpret i a possibilistic sese the membership fuctio or degree of typicality. The Possibilistic C-Meas algorithm I (PCM-I) is based o a modificatio of the objective fuctio of FCM. I this case, oe must supply the values of some parameters such as the fuzzifier parameter, ad others regulatig the weight of the spread of membership fuctios.the Possibilistic C-Meas algorithm II (PCM-II) is based o modificatio of the cost fuctio of the HCM (istead of the FCM) i order to avoid, i this way, the determiatio of the fuzzifier parameter. The objective fuctio of the PCM-II cotais two terms as show below. c c J m (U,v)=Σ Σ (u jk )E j (x k )+ Σ ρ j Σ(u jk l u jk -u jk ) (1) j=1 j=1 where, J m (U,v) is the objective fuctio E j (x k ) is the Euclidea distace betwee x k ad v j. ρ j is the possibilistic costrait. U= [u jk ] is the c x fuzzy partitio matrix. c is the umber of cluster ceters. is the umber of data poits. The first oe is the objective fuctio of the HCM, while the secod is a regularizig term, forcig the values u jk to be greatest as possible, i order that poits with a high degree of typicality with respect to a cluster may have high u jk values, ad poits ot very represetative may have low u jk values i all clusters. I the above equatio E j (x k )= x k -y j 2 is the square of the Euclidea distace, ad the parameter ρ j depeds o the distributio of poit i the j-th cluster. Σ (u jk ) m x k v j =, j (2) Σ (u jk ) m u jk =exp (- E j (x k )/ ρ j ), j,k (3) where m ε (1, ) is a cotrol parameter of fuzziess. This theorem provides the coditios eeded i order to miimize the cost fuctio. The above two equatios ca be iterpreted as formulas for recalculatig the membership fuctios ad the cluster ceters. A bootstrap clusterig algorithm is ayway eeded before startig PCM i order to obtai a iitial distributio of prototypes i the feature space ad to estimate some parameters used i the algorithm. By cosiderig a FCM bootstrap for the PCM, the followig defiitio of ρj ca be used. Σ (u jk ) m E j (x k ) ρ j = K for all j (4) Σ (u jk ) m where, m is the fuzzifier parameter used by the FCM, ad K is a proportioal parameter. This defiitio makes ρ j proportioal to the mea value of the itracluster distace, ad critically depeds o the choice of K. PFCM Algorithm 1. Fid U, v (r) usig FCM or ay other clusterig algorithm. 2. Fid the possibilistic costrait ρ j usig the equatio (4).

3 3. Fid U usig the equatio (3). 4. Fid the cluster ceters v (r + 1) usig the equatio (2). 5. Fid the distace matrix D usig the equatio E j (x k ) = x k - v j 2 (5) 6. Check for covergece If max v(r) - v(r+1) < E Stop else repeat the steps Self Estimatio Algorithm If the umber of clusters is maually specified the segmetatio may ot be effective. Hece there must be a system to calculate the robust umber of clusters. The self estimatio algorithm used here fids the Euclidea distace betwee the differet cluster ceters. If the maximum Euclidea distace betwee the cluster ceters is greater tha the specified value, the the umber of cluster ceters is icreased by oe else the clusters are merged.. A alterate method, i which we start with a high umber of clusters ad merge o the result of the iteratios, ca also be used. 2.3 Peak Sigal to Noise Ratio Sigal-to-oise (SNR) measures are estimates of the quality of a recostructed image compared with a origial image. The basic idea is to compute a sigle umber that reflects the quality of the recostructed image [4]. Recostructed images with higher metrics are judged better. I fact, traditioal SNR measures do ot equate with huma subjective perceptio. Several research groups are workig o perceptual measures, but for ow sigal-to-oise measures are used because they are easier to compute. Also to be oted that higher measures do ot always mea better quality. The actual metric that is computed i this work is the peak sigal-to-recostructed image measure, which is called PSNR. Assume a source image f(i,j) is give that cotais M by N pixels ad a recostructed image F(i,j) where F isrecostructed by decodig the ecoded versio of f(i,j). Error metrics are computed o the lumiace sigal oly so the pixel values f(i,j) rage betwee black (0) ad white (255). First the mea absolute error(mae) of the recostructed image is computed as follows 1 M N MAE= Σ Σ F(i,j)-f(i,j) (6) MN i=1 j=1 The summatio is over all pixels. PSNR i decibels(db) is computed by usig PSNR = 20 log 10 (255 2 / MAE). Figure 1: Resized Satellite Image Figure 2: Clustered image with 4 classes Figure 3: Extracted output with spectral Sigature of Road Typical PSNR values rage betwee 20 ad 40. They are usually reported to two decimal poits (e.g., 25.47). The actual value is ot meaigful, but the compariso betwee two values for differet recostructed images gives oe measure of quality. The MPEG committee used a iformal threshold of 0.5 db PSNR to decide whether to icorporate a codig optimizatio because they believed that a improvemet of that magitude would be visible. Some defiitios of PSNR use 255/RMAE rather tha /MAE. Either formulatio will work because we are iterested i the relative compariso, ot the absolute values.i our assigmets we used the defiitio give above. Fig.1 shows the resized satellite image which shows a ed of road at Hawaii. This image is processed usig possibilistic fuzzy c meas. The resultat image is show i Fig.2. The o. of clusters obtaied i the resultat image by self estimatio algorithm, is 4. The peak sigal to oise ratio of processed image is Fig.3 shows extracted output without applyig mathematical morphology. Comparig it with origial image we ca see that the extracted output shows the road as well as the soils.

4 3. Mathematical Morphology Mathematical morphology is a image processig tool used for extractig features of iterest. Mathematical morphology is a set theory approach, which is based o geometrical shape. It uses set operatios such as uio, itersectio ad complemetatio. Mathematical morphology operatios iclude dilatio, erosio, opeigs, closigs ad the derived operatios such as Hit or Miss, thiig, their properties ad their use. The objective of the work udertake is to develop a algorithm for automated road etwork detectio from high resolutio images usig mathematical morphology. Firstly a road is segmeted from backgroud. The mathematical morphology tools used are graulometry ad trivial opeig. A graulometry aalysis of objects i image is performed ad the size iformatio of road etwork is obtaied. The road is extracted preprocessed images ad differetiated other features with similar properties as roads usig Trivial Opeig ad closig. It is doe based o the size iformatio provided by the graulometry. 3.1 Graulometry Graulometry[6] is oe of the most useful ad versatile tools of morphological image aalysis. Graulometry techiques fid wide rage of applicatios i feature extractio, texture characterizatio, size estimatio image segmetatio etc. The cocept behid graulometry is to determie the size ad shape distributio of objects preset withi a image. Graulometry is a field that deals pricipally with determiig the size distributio of particles i a image. Opeig operatios with structurig elemets of icreasig size are performed o the origial image. The differece betwee the origial image ad the its opeig is computed after each pass whe a differet structurig elemet is completed. At the ed of the process, these differeces are ormalized ad the used to costruct a histogram of particle size distributio. This approach is based o the idea that opeig operatios of a particular size have the most effect o regios of the iput image that cotai particles of similar size. Thus, a measure of the relative umber of such particles is obtaied by computig the differece betwee the iput ad output images. These measures ca be used as shape ad size sigature of the origial image ad ca be plotted as a patter spectrum. 3.2 Morphological Trivial Opeig Morphological Trivial Opeig is defied as oe, which provides a practical mea of object detectio ad idetificatio. It does ot affect the shape ad size of the objects of iterest. Let X be a image, {X() = 1, 2, 3,,N} is a series of coected compoets i the image, x(i) is a poit i X(i). We defie the trivial opeig with a criterio T, as follows. X(i), if X(i) satisfies the criterio T TO = φ, otherwise where TO is the trivial opeig associated with criterio T. It is morphological opeig, because it is idempotet, atiextesive ad icreasig. I image processig, this operatio uses the criterio T to filter the coected compoets that satisfy the criterio T. Trivial opeig based o criterio T provides a practical meas of object detectio ad derived area opeig from coected ad trivial opeig to fid the coected regios i a image with a certai area. Trivial opeig does ot affect the shape ad size of the coected regios that are preserved because it preserves the etire coected regios. I our desig we have used the structural elemet disc of size 1 ad 2 as the threshold i.e. criterio T. Sice the width of street is less tha that of the mai road, opeig operatio with the structure elemet whose size smaller tha mai road but slightly larger tha that of street remove small paths. Further it removes the paths coectig road etwork to the houses. The side effect of opeig is that it also removes the road parts, which are uder shadow caused by houses ad trees. It is ecessary to develop tools to recostruct these road parts. Mathematical morphology based widow operatio is desiged for this purpose. 4. Software Desig The steps ivolved i the road extractio algorithm are as follows. 1. Load the image from which road has to be extracted 2. Segmet the image usig PFCM 3. Extract spectral iformatio of road 4. Perform Graulometry 5. Remove oises ad small path usig trivial opeig 6. Close the small opeigs caused by trivial opeig 7. Display the extracted road 4.1 Estimatio Algorithm The self estimatio algorithm for fidig o. of clusters i the image automatically is give below 1. Start with c = 2 2. Fid the cluster ceter matrix v usig equatio (2) 3. Compute Euclidea distace betwee cluster ceter values, q 4. If mi ( q ) > E, icremet c ad repeat steps 2, Image Segmetatio The followig steps do the image segmetatio 1. Load the image file to be segmeted 2. Get the mode of operatio 3. If the mode is maual get the o. of clusters 4. If the mode is automatic calculate the o. of clusters usig self estimatio algorithm 5. Usig PFCM segmet the image 6. Determie PSNR

5 7. Display the segmeted image ad its PSNR value 5. Coclusio Figure4: Origial Image I this paper we have preseted a ew method for road extractio. It has bee tested o high resolutio satellite ad aerial photos. The result shows that mathematical morphology provides a effective tool for automated road etwork detectio. Fig.4 shows the resized satellite image Beijig, Chia with 1 meter resolutio, which is atural colour multispectral Ikoos image. This image is clustered usig possibilistic fuzzy c meas. The resultat image is show i Fig.5. By self estimatio algorithm, the o. of clusters obtaied i the resultat image is 7. The peak sigal to oise ratio of processed image is From this clustered image objects of road like spectral values are extracted. It is show i fig.6. It is see from fig.6 that shadows, small paths are also extracted. Some of the road parts are also removed. I Fig.7 the extracted road is show. For road extractio trivial opeig bee used. The structural elemets used are disk ad rectagle. For closig the paths lie structure has bee used. Eve after applyig morphological methods some part of road is missig. This is because image resizig which resulted i poor image resolutio. It takes loger time for the system to process large image. Hece, resizig bee doe. Some part of the image, which has the same spectral feature of the road, is also obtaied. But compared with the PFCM segmeted image of Fig.6 i the image of Fig.7 the road is quite visible. Figure 5: Clustered Image Figure 6: Extracted output with spectral Sigature of Road Figure 8: Origial Image Fig.8 shows the resized satellite image of Rome, Italy with 1meter resolutio. It is a atural colour multispectral Ikoos image Figure 7: Extracted Road Network Figure 9: Clustered Image

6 Figure 6: Extracted output with spectral Sigature of Road Figure 7: Extracted Road Network Figure 12: Extracted Road Network. This image clustered usig possibilistic fuzzy c meas is show i Fig.9. The o. of clusters obtaied i the resultat image are 4. The peak sigal to oise ratio of processed image is From the clustered image objects of road like spectral values are extracted is show i fig.10. As with the Beijig image the road etwork see i fig.11 after processig with morphological methods is better tha that of fig.10. Sice the iput picture quality is better the road is see well. It is better tha that of image of Beijig. Fig. 12 shows the extracted road from image of Fig.1 The proposed method bee tested with may satellite images ad it is prove to be effective. The algorithm used works well with high resolutio elogated road images. The reductio of oise usig the PFCM algorithm shows its effectiveess. The program has also bee tested for its cosistecy ad its reliability. This method ca be used i several idustrial applicatios such as for auto recogitio ad for military purposes. It is importat to provide military ad other groups with accurate, up-todate maps of the road etworks i ay regio of the world. [1] Alla G. Habury ad Jea Serra, Morphological Operators o the Uit Circle, IEEE Trasactios o Image Processig, VOL. 10, NO. 12, pp ,dec [2] Schupp, S. Elmoataz, A. Fadili, J. Herli, P. Bloyet D. Image segmetatio via multiple active cotour models ad fuzzy clusterig with biomedical applicatios i 15th Iteratioal Coferece o Patter Recogitio, Proceedigs, pp , Vol.1,2000. [3] Sogca Che, Daoqiag Zhag, Robust image segmetatio usig FCM with spatial costraits based o ew kerel-iduced distace measure i IEEE Trasactios o Systems, Ma ad Cyberetics, Vol.34,No.4,pp: ,Aug [4] Zhou Wag ad Dapeg Zhag, Restoratio of Impulse Noise Corrupted Images Usig Log-rage Correlatio i IEEE Sigal Processig Letters, vol. 5, o. 1, pp. 4-7, Ja [5] R. Krishapuram, J.M. Keller. The possibilistic c meas: isights & recommedatios. IEEE Tras. Fuzzy Systems 4: , [6] Scott T. Acto, ad Dipti Prasad Mukherjee,Scale Space Classificatio Usig Area Morphology, IEEE Trasactios o Image Processig, Vol. 9, No. 4, pp , April [6] Ouzouis, G.K. ad Wilkiso, M.H.F., Couterig over segmetatio i partitioig-based coectivities, IEEE Iteratioal Coferece o Image Processig, ICIP Volume 3, pp ,Sept [7] B.Sowmya ad Sourav Bhattacharya Colour Image Segmetatio Usig Fuzzy Clusterig Techiques i the iteratioal coferece IEEE Idico 2005 orgaised by IEEE Cheai Chapter at IIT, Cheai o December [8] Grue, A. ad H. Li, Road Extractio from aerial ad satellite images by dyamic programmig, ISPRS Joural of Photogrammetry ad Remote Sesig, pp , Vol. 50, No. 4, Refereces