AUTOMATIC ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES USING NEURAL NETWORKS, TEXTURE ANALYSIS, FUZZY CLUSTERING AND GENETIC ALGORITHMS

AUTOMATIC ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES USING NEURAL NETWORKS, TEXTURE ANALYSIS, FUZZY CLUSTERING AND GENETIC ALGORITHMS M Mokhtarzade a, *, M J Valadan Zoej b, H Ebad b a Dept of Geomatcs Engneerng, KN Toos Unversty, Tehran, Iran-m_mokhtarzade@yahoocom b Dept of Geomatcs Engneerng, KN Toos Unversty, Tehran, Iran-(ValadnZouj, Ebad)@kntuacr Commsson III KEY WORDS: Road Extracton, Neural Networks, Co-occurrence Texture Analyss, Fuzzy Clusterng, Vectorzaton ABSTRACT: In ths artcle, a new method for road extracton from hgh resoluton Quck Brd and IKONOS pan-sharpened satellte mages s presented The proposed methodology conssts of two separate stages of road detecton and road vectorzaton Neural networks are appled on hgh resoluton IKONOS and Quck-Brd mages for road detecton Ths paper has endeavoured to optmze neural networks functonalty, usng a varety of texture parameters These texture parameters had dfferent wndow szes and grey level numbers, not only from source but also from pre-classfed mage Road vectorzaton s based on the dea of road raster map clusterng obtaned from the prevous road detecton stage In ths step, despte of genetcally guded clusterng, a new flexble clusterng methodology s proposed for road key pont dentfcaton The last step of road key pont connectng s carred out based on the obtaned nformaton from a fuzzy shell based clusterng The accuracy assessment of the obtaned vectorzed road network proved the ablty of the proposed method n sub-pxel road extracton 11 Road Extracton 1 INTRODUCTION The presence of hgh resoluton satellte mages and ther potental to be used n wde varety of applcatons such as preparng and updatng maps have made the automatc extracton of object, especally roads and buldngs, a new challenge n remote sensng Tradtonally, road extracton from aeral and satellte mages has been performed manually by the operator Consderng the fact that ths method was costly and tme consumng the effcency was by no means very hgh Automatc road extracton provdes means for creaton, mantanng, and updatng transportaton network It also provdes data bases for traffc management, automated vehcle navgaton and gudance Vgorous methods have been proposed for automatc and semautomatc extracton of road networks from satellte mages Recently, these methods are more focused on hgh resoluton satellte mages due to ther outstandng characterstcs n mappng from space 12 Related Researches Revew A comprehensve revew on the proposed methods for road extracton s found n (Mena, 2003) where these methods are categorzed from dfferent aspects A comprehensve reference lst s also accessble (Mohammadzadeh et al 2006) proposed a new fuzzy segmentaton method for road detecton n hgh resoluton satellte mages wth only a few number of road samples Afterward by usng an advanced mathematcal morphologcal operator, road centrelnes were extracted A road detecton strategy based on the neural network classfers was ntroduced by (Mokhtarzade and Valadan, 2007) where a varety of nput spectral parameters were tested on the functonalty of the neural network for both road and background detecton The dea of geometrcal and topologcal analyss of hgh resoluton bnary mages for automatc vectorzaton of segmented road networks was presented n (Mena, 2006) Robust polynomal adjustment was used for geometrcal analyss whle mathematcal morphologcal operators were appled n topologcal adjustment Recently, many researchers have tested the dea of usng contextual nformaton for mprovng segmentaton process of road regons The research presented by (Mena and Malpca, 2005) s a good example for explotng texture nformaton n road extracton In hs paper, Mena and Malpca, performed a GIS updatng usng the pre-exstng vectoral nformaton and the RGB bands of hgh resoluton satellte or aeral mages The bnary segmentaton performed n hs research was based on Texture Progressve Analyss the three level of texture statstcal evaluaton beng developed based on evdence theory framework Fnally, through skeleton extracton and * Correspondng author 549

The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences Vol XXXVII Part B3b Bejng 2008 morphologcal operators, the obtaned bnary mage was vectorzed Manually Extracted Roads Pan Sharpened HR Image Tranng Data Furthermore, (Zhang and Coulogner 2006) evaluated the effectveness of angular texture sgnature to dscrmnate among parkng lots and roads usng hgh resoluton satellte mages In ther research, spectral and textural nformaton were used separately for detecton of roads and for elmnatng of nonroad pxels respectvely In ths research, a two stages road extracton methodology s presented conssted of road detecton and a vectorzaton processes Accuracy Assessment Road Detecton Usng NN Road Raster Map Road Vectorzaton Usng Fuzzy Clusterng Road detecton s performed on hgh-resoluton pan-sharpened RGB Quck Brd and IKONOS satellte mages, usng texture parameters n artfcal neural network algorthms The vectorzaton procedure s made up of two steps of road key pont dentfcaton and generatng road connectons Road key pont dentfcaton s performed usng c-means clusterng on road raster map For ths reason, at frst the possblty of genetcally guded clusterng s evaluated Then a novel methodology for flexble road key pont determnaton, called ncreasng ellpse, s proposed When road key ponts as the centre of dfferent adjacent road patches are determned, a fuzzy shell clusterng provdes the clues for establshment of road segments In secton 2, the proposed methodology for both steps of road detecton and vectorzaton are descrbed Secton 3 presents the obtaned practcal results and accuracy assessment parameters 2 METHODOLOGY Road networks n hgh resoluton satellte and aeral mages are presented as elongated homogeneous areas havng a dstnct brghtness dfferences from the background Therefore, the common practce of automatc road extracton from hgh resoluton satellte mages, as t s mplemented n ths research, conssts of two man steps enttled as Road Detecton and Road Vectorzaton Fgure 1 shows the dagram of the mplemented methodology of road extracton n ths research The frst step of road detecton concentrates on dscrmnatng between road and background pxels It s consdered as an mage segmentaton process where a meanngful value s assgned to each mage pxel that can be used as the crteron to dstngush between road and non-road pxels In ths research, neural networks are appled for road detecton where dfferent spectral and texture parameters are uses as ther nput parameters The result of road detecton s a bnary mage, representng all detected road pxels whch s called road rater map The vectorzaton step ams at extractng the road network centrelne and ts sdes from the prevously produced road raster map Qualty Control Parameters Vectorzed Road Centerlnes Fgure 1 The methodology of road extracton In ths research, a novel method of road raster map clusterng s developed to dentfy road key ponts, where a fuzzy shell clusterng provdes the requred nformaton to generate vectorzed road networks It should be mentoned that the vectorzaton step can be mplemented ndependently from the road detecton step Hence, t could be appled on any road raster map generated from dfferent road detecton methodologes In the followng, the detaled methodologes for each of these two man steps are explaned n dfferent sectons 21 Road detecton In ths research, the most common back propagaton neural networks are used as the mage classfers for road detecton Fgure 2 shows the desgned neural network structure for ths reason BP NN Input Image x Texture Wndow Interest Pxels x 1 2 x n Road Feature Vector CAD Based Systems [ 0,1 ] Fgure 2 Road detecton usng neural networks As shown n fgure 2, the nput layer conssts of neurons the same number as road feature vector dmenson where each nput neuron s n charge of recevng one normalzed nput parameter Only one hdden layer s desgned n the neural network whle the number of neurons n ths layer can be vared The output layer has only one neuron, expressng the neural network s response n the range of [0, 1] as the road assocaton value for the nterest pxel After applyng the traned neural network on the entre nput mage, the road raster map can be produced assumng a threshold on the road assocaton value of nput mage pxels I 550

The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences Vol XXXVII Part B3b Bejng 2008 A detaled survey on road detecton usng artfcal neural networks can be found n (Mokhtarzade and Valadan, 2007) Besde spectral values, textural behavour of road pxels as beng homogeneous areas s the most outstandng road pxel property n the hgh resoluton satellte mages Hence, ncorporatng spectral and textural parameters for road feature vector generaton s gong to be mplemented n ths research The nput mage was converted to dfferent levels of ntensty values and a varety of co-occurrence texture parameters, consstng of Energy, Entropy, Contrast, Homogenety, were extracted from dfferent wndow szes Except the source satellte mage, the prelmnary road raster map generated from a smple neural network (traned only wth spectral nformaton) was also used as the nput mage for texture analyss Dfferent combnatons of texture and spectral parameters were put n the road feature vector and the functonalty of the neural network was evaluated comparng the road raster map and the reference manually determned road pxels It was determned that usng all four texture parameters, extracted from the prelmnary road raster map, accompaned by the spectral nformaton of the source mage can make the optmum road feature vector Ths road feature vector could mprove both road and background detecton ablty of neural network Tranng data Mult-spectral Image Neural Network Classfed Image Texture Analyss Fgure 3 shows the dagram of the proposed methodology for road detecton usng texture parameters n neural networks The detaled explanaton about ths subject can be found n (Mokhtarzade, et al, 2007) 22 Road vectorzaton The vectorzaton methodology mplemented n ths research s based on the dea of road raster map clusterng, frst ntroduced by (Doucette et al, 2001) Ths process can be dvded nto two man steps as road key pont dentfcaton and road key pont connecton 221 Road Key Pont Identfcaton In the frst step of road key pont dentfcaton, the road raster map, obtaned from the road detecton process, s segmented nto dfferent adjacent road patches based on mage space clusterng algorthm When the clusterng s performed, the centrod of each road patch s regarded as a road key pont In (Doucette et al, 2001), a K-mean crsp clusterng algorthm wth user defned cluster number was appled on hgh resoluton road raster map usng a unform dstrbuton of cluster centres Ths tradtonal method can produce acceptable results provded that the avalable roads n the raster map share rather the same dstrbuton n the mage and have smlar wdths Furthermore, the ntal number of clusters, determned by tral and error, has a major nfluence on the success of ths method In order to overcome the mentoned shortcomngs of the tradtonal method, especally the nfluence of ntal cluster number, a genetcally guded clusterng wth a varable length chromosome ntroduced n (Malay K et al, 2005) was developed Consderng the elongaton property of road patches, an ellpse was used to represent clusters shape In ths manner, the chromosome structure was desgned as fve-gene blocks where each block represents an ellpse poston (x, y), shape (a, b) and orentaton parameters Fgure 4 shows the structure of the desgned chromosome for the case of havng M clusters CON ENE ENT HO Clusterng Road Detecton Segmented Image Optmzed Road Raster Map AND Fgure 3 Road detecton usng texture parameters of prelmnary road raster map Fgure 4 Chromosome structure for genetcally guded ellpse clusterng The proposed genetc ftness functon used n ths research s shown n equaton 1 FP = 1 Ftness _ Functon = (1) 2 M th Where FP shows the flled percent of ellpse computed as below: N FP = (2) 4ab th Where N represents the number of road pxels assgned to cluster (road patch) M 551

The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences Vol XXXVII Part B3b Bejng 2008 Besde genetcally guded road key pont dentfcaton, the dea of ellpse clusterng was developed to another novel method called ncreasng ellpse In ths method, for each road patch representatve ellpse, the followng parameters are determned: Area = 4 a b Area N Dff % = Area NM Sample% = N (3) th In ths equaton Dff % shows the percent of the ellpse area dfference wth the area covered by ts assocated road pxels Also Sample% s a measure of the expected assgned road th pxels to the road patchbased on the above ntroduced ellpse parameters, road patches are categorzed as Nose, Concde and Under-Evaluaton patchesnose patches are those clusters havng the followng condtons: ( % 05) ( Dff % MeanDff %% + 25σ Dff %) AND Sample Sample% 01 (4) b 2 σ Dff In equaton 5, Mean and are the mean and standard Dff % % devaton of Dff % values of all M clusters Concde patches are those clusters satsfyng the condtons expressed n equaton 5 The patch s not markes as Nose patch Dff % Threshold (5) a < 4b Under-Evaluaton patches are the rest of clusters not marked as nose nor as concde patchesfgure 5 shows the methodology of the nvented ncreasng ellpse clusterngthe termnaton condton of ths procedure s to have no ender-evaluaton patches whle all of them are categorzed as concde or nose patches Road Raster Map Consder one arbtrary cluster (M=1) K-mean Clusterng Cluster evaluaton to dentfy Nose and concde patches Save concde patches nf and omt ther correspondng road pxels from the road raster map Termnaton Condtons? Yes Is ths the frst excluson of nose patches? Yes Exclude Nose patches samples Add a new cluster at the poston of the utmost sample of the patch havng the max Dff% (M=M+1) Fgure 5 Increasng ellpse road vectorzaton The termnaton condton of ths procedure s to have no enderevaluaton patches whle all of them are categorzed as concde or nose patches 222 Road Key Pont Connecton: In order to make correct connectons between the dentfed road key ponts, the presence of common road pxels between adjacent road patches were used as the connecton gude For ths reason, a fuzzy shell clusterng was mplemented on the pre-determned ellpse, representatve of concde patches Vague samples, whch are road pxels belongng to more than one road patch wth rather the same membershp values, were determned based on the obtaned membershp matrx Usng the centrod of vague road pxels as the mddle pont of key ponts, the correspondng connectons were generated No No Centre of concde patches as road key ponts 3 PRACTICAL RESULTS In order to evaluate the functonalty of the road extracton method proposed n ths research, two sub-samples of pansharpened Quck Brds and IKONOS mages from Bushehr harbor and Ksh Island n Iran were used as case study Fgures 6 and 7 show the source nput mages wth ther manually 552

The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences Vol XXXVII Part B3b Bejng 2008 produced reference mages appled n accuracy assessment procedure RCC and BCC, stand for Road/Background Detecton Correctness Coeffcent respectvely, are the average of correct neural network response for road and background detecton by comparson the manually produced reference mage (Fgures 6- b and 7b) Regardng the dfference between the neural network response and ts true expected response (0 for background and 1 for road pxels) as the error values, the Root Mean Square Error (RMSE) can be computed as the thrd accuracy assessment parameter Fgure 8 show the neural network road detecton results for nput pan-sharpened mages of Fgures 6a and 7a These gray scale mages are produced by multplyng the normalzed neural network output by 255 a b Fgure 6 Pan-sharpened Quck Brd mage of Bushehr harbor and ts manually produced reference mage a b a b Fgure 7 Pan-sharpened IKONOS mage of Ksh Island and ts manually produced reference mage In the followng sectons, the practcal results of dfferent step of road extracton are presented accentuatng on practcal aspects of the mplementaton 31 Implementaton Results of Road Detecton Road detecton was performed usng an artfcal neural network consstng of 7 neuron n ts nput layer n charge of recevng 3 spectral values (R, G, B) and 4 textural parameters as explaned n secton 21 The hdden layer was made up of 10 neurons and the output layer, havng only one neuron, was desgned to show the response of neural network No Texture Parameter Usng Texture Parameters Sample #1 Sample #2 RCC BCC RMSE RCC BCC RMSE 8236 9353 0172 7705 9086 0259 9354 9631 0106 8077 9615 0196 Table1 Accuracy assessment of road detecton procedure About 500 road and 500 background pxels were selected from each nput mage to be used n neural network tranng stage An adaptve strategy was appled for learnng rate and momentum parameters to stablze the tranng stage of the neural network In order to evaluate the performance of the road detecton procedure, three qualty control parameters, RCC, BCC and RMSE were used c d Fgure 8 Neural network road detecton results In Fgure 8, the left sde mages (8a and 8d) show the obtaned result of smple neural network where no texture parameter s used Rght sde mages of Fgure 8 (8b and 8d) depcts the output of the proposed neural network structure where texture parameters of the prelmnary road raster maps ( Fgures 8a and 8c) are used besde spectral nformaton for neural network nput parameters set generaton Table 1 show the obtaned accuracy assessment parameters for both cases where the nput source mage of Fgures 6a and 7a are called sample#1 and Sample#2 The presented accuracy assessment parameters n Table 1 show that both road and background detecton ablty of the textural mproved neural network are mproved and thus the effcency of the proposed road detecton methodology n ths research s approved 32 Implementaton Results of Road Vectorzaton The obtaned results of mproved neural networks (Fgures 8b and 8d) were converted to road raster map puttng a threshold on the grey scale values The obtaned road raster maps were used n the road vectorzaton process descrbed n secton 22 At the frst attempt, genetcally guded road key pont determnaton was performed on a smulated road raster map Although the obtaned result was acceptable, the computaton tme, even for the small sze smulated road raster map, was 553

The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences Vol XXXVII Part B3b Bejng 2008 hgh The computaton tme problem reduced the practcal attractveness of ths method and the alternatve method pf ncreasng ellpse was followed The threshold value for concde path determnaton was chosen to be 02 It should be mentoned that ths value s selected based on the S/N rato of the nput road raster map Fgures 9 and 10 show the obtaned result of road key pont dentfcaton for two nput mages of Fgures 6a and 7a n ths mages, each road patch wth ts representatve ellpse s presented n a separate colours Fgure 11 Road vectorzaton of nput mage 6a Fgure 9 Road key pont dentfcaton of nput mage 6a Fgure 12 Road vectorzaton of nput mage 7a In order to evaluate the performance of road vectorzaton procedure, three accuracy assessment parameters were desgned and computed whch are called Mean Devaton, Completeness and RMSE Mean devaton s computed as follows: Devaton Area Mean Devaton = Road Legth (6) Fgure 10 Road key pont dentfcaton of nput mage 7a A fuzzy shell clusterng was performed based on the predetermned concde road patches and vague road pxels were determned These pxels are shown n Fgures 9 and 10 wth yellow colourfnally, consderng the centrod of vague road pxels, the road connectons were generated as shown n Fgures 11 and 12 for nput source mages of Fgures 6a and 7q respectvely Where, devaton area s the area between the dentfed road centerlne and the manually reference extracted road vector map Completeness, as the second vectorzaton accuracy assessment parameter, represents the length percent of the extracted road network n the nput mage Table 2 shows the obtaned accuracy assessment parameters for both sample#1 and Sample #2 Mean Devaton Max Devaton Completeness RMSE Sample #1 053 200 88% 055 Sample #2 038 141 84% 049 Table 2 Accuracy assessment parameters of road vectorzaton procedure 554

The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences Vol XXXVII Part B3b Bejng 2008 4 CONCLUSIONS In ths artcle, a road extracton methodology from hgh resoluton satellte mages s proposed The frst step called road detecton was performed based on NN classfers It was dscovered that usng texture parameters of a bnerzed pre-determned road raster map ntegrated wth spectral nformaton of ndvdual pxels can mprove both road and background detecton ablty of neural networks In the second step of road vectorzaton, genetc algorthms dd not show enough attractveness as they are qute tme consumng for mage clusterng A novel clusterng algorthm was proposed for road key pont dentfcaton wch s based on shape nterpretaton of road patches Then the obtaned road key ponts were connected consderng the adjacency nformaton obtaned form a fuzzy clusterng The desgned methodology was performed on dfferent pansharpened IKONOS and Quck Brd sample mages and the road extracton ablty the proposed method was approved REFERENCES Doucette, P, Agours, p, Stefands, A, and Musav, M, 2001 Self-organsed clusterng for road extracton n classfed magery, ISPRS Journal of Photogrammetry and Remote Sensng 55( 5/6), pp 347-358 Malay K Pakhra, Sanghamtra Bandyopadhyay, Ujjwal Maulk, 2005 A study of some fuzzy cluster valdty ndces, genetc clusterng and applcaton to pxel classfcaton, Fuzzy Sets and Systems 155 (2), pp 191 214 Mena, JB, 2003 State of the art on automatc road extracton for GIS update: a novel classfcaton Pattern Recognton Letters, 24(16), pp 3037 3058 Mena, JB, and Malpca JA, 2005 An automatc method for road extracton n rural and sem-urban areas startng from hgh resoluton satellte magery Pattern Recognton Letters 26(9), pp 1201-1220 Mena, JB, 2006 Automatc vectorzaton of segmented road networks by geometrcal and topologcal analyss of hgh resoluton bnary mages Knowledge based systems 19(8), pp 704-718 Mohammadzadeh, A, Tavakol, A, and Valadan Zoej, MJ, 2006 Road extracton based on fuzzy logc and mathematcal morphology from pan-sharpened IKONOS mages The Photogrammetrc Record, 21(113), pp 44-60 Mokhtarzade, M, Ebad, H, and Valadan Zoej, MJ, 2007 Optmzaton of Road Detecton from Hgh-Resoluton Satellte Images Usng Texture Parameters n Neural Network Classfers Canadan Journal of Remote Sensng 33(6), pp 481-491 Mokhtarzdae, M, and Valadan Zoej, MJ, 2007 Road detecton from hgh resoluton satellte mages usng artfcal neural networks Internatonal journal of appled earth observaton and geonformaton, 9(1), pp 32-40 Zhang, Q, and Coulogner, I, 2006 Beneft of the angular texture sgnature for the separaton of parkng lots and roads on hgh resoluton mult-spectral magery Pattern Recognton Letters 27(9), pp 937-946 555

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