A MULTIRESOLUTION REMOTELY SENSED IMAGE SEGMENTATION METHOD COMBINING RAINFALLING WATERSHED ALGORITHM AND FAST REGION MERGING

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1 A MULTIRESOLUTION REMOTELY SENSED IMAGE SEGMENTATION METHOD COMBINING RAINFALLING WATERSHED ALGORITHM AND FAST REGION MERGING Min Wang a a Key Laboratory of Virtual Geograpi Environment (Nanjing Normal University), Ministry of Eduation, Nanjing, Jiangsu, 0046, Cina - sysj098@6.om KEY WORDS: Multiresolution, Image, Segmentation, Metod, Algoritms ABSTRACT: Nowadays objet oriented image analysis beomes a ot issue in te field of image proessing and interpretation beause of its more robust noise removing ability, more abundant image features and expertise knowledge involved in analysis. Te first and most important step of objet oriented image analysis is image segmentation, wi segments an image into many visual omogenous parels. Based on tese parels, wi are objets not pixels, more features an be involved wi failitates te sueeding image interpretation. In tis work, a multi-resolution image segmentation metod ombining spetral and sape features is designed and implemented wit referene to te basi ideas of ecognition, a famous objet oriented image analyzing software pakage. Te algoritm inludes te following steps. ) Te initial segmentation parels, so alled te sub feature units are obtained wit rainfalling watersed algoritm for its fast speed and pretty good initial segmentation effets. ) A fast region merging tenique is designed to merge tese sub feature units in a ierary way. A sale parameter is used to ontrol te merging proess, wi stops a merge wen te minimal parel merging ost exeeds its power. A multi-resolution segmentation an be implemented wit different sale parameters, for smaller sales means less ost wile merging wi reate smaller parels, and vie versa. Several experiments on ig spatial resolution remotely sensed imagery are arried out to validate our metod.. INTRODUCTION Nowadays objet oriented image analysis beomes a ot issue in te field of image proess and interpretation. Te basi idea of tis kind of metod is to segment an image into parels, extrat features from te parels, and ten omplete te wole image interpretation wit lassifying te features. Te main advantage of objet oriented image analysis lies in tat it deals wit parels, wi are objets, not pixels, wi auses more abundant features and spatial knowledge involved in analysis. Besides, wit more robust pepper noise removing ability, it also brings more ompreensible interpretation results (Aplin et al., 999). ecognition (Definiens, 007) is a world famous objet oriented image analysis software, in wi te multiresolution image segmentation metod (Baatz et al., 007) is a key and patented tenology, wose tenologial details asn t been opened to te publi yet. In order to implement our objetoriented image analysis software pakage for information extration from ig spatial resolution remotely sensed imagery, we design and implement a multiresolution image segmentation metod ombining spetral and sape features, wit referene to te basi ideas of ecognition. Our metod is validated wit several suessful experiments on ig spatial resolution remotely sensed imagery.. METHOD PRINCIPLE AND STEPS Wen grouping pixels into very small sub feature units at te beginning stage of our algoritm, it s of little use to import sape feature. In our metod, an initial segmentation is firstly arried out only wit spetral features to obtain te sub feature units. Sape an ten be introdued into te algoritm to ontrol te furter merging of tese feature units wit suitable size. We use rainfalling watersed algoritm to reate tese sub feature units for its fair segmentation preision and very fast algoritm speed, wi is important for proessing remotely sensed imagery ommonly wit large data volumes. But mainly due to image noise, most watersed algoritms inluding rainfalling watersed ave a serious over-segmentation sortoming. Sometimes it auses tat tere exist a large number of very small parels sattered in te output segmentation. A pre or post image proessing sould be arried out to remove tis adverse influene for furter analysis. In our work, we take te latter one, wi deals wit tese very small units in a unified region merging way.. SUB FEATURE UNIT EXTRACTION Watersed algoritm is a pretty good image segmentation metod based on image grey values. A lassial implementation of watersed is based on immersion simulation [Vinent et al., 99]. Watersed segmentation an also be implemented in a so alled rainfalling manner. Its priniple is to find a steepest routine of every pixel on te simulated image topograpi surfae, and a watersed base is defined as te pixel set wose downriver routine ends at a same altitude loal minimum. Te algoritm inludes two main steps: ) flooding stage: flood te image wit some altitude tresold to reate partial billabongs to redue te ig frequeny signal parts aused by noises so to suppress te over-segmentation of ommon watersed algoritms; ) rainfalling stage: in order to lassify a pixel wi asn t fallen into ertain billabong, a rolling down route of a raindrop on tat pixel is simulated, and all te pixels under tis route will be grouped into one lass (belong to a same watersed). After all tese pixels are labelled, te segmentation will be terminated. A ritial issue of rainfalling watersed segmentation implementation lies in orretly dealing wit te loal levels embedded in te slopes [Stoev, 000]. 3

2 Te International Arives of te Potogrammetry, Remote Sensing and Spatial Information Sienes. Vol. XXXVII. Part B4. Beijing 008 Aording to Smet s work [Smet et al., 000], te effiieny of rainfalling watersed segmentation is superior remarkably to watersed algoritms based on immersion simulation. For tis reason, te rainfalling watersed algoritm is osen to initially segment an image into a set of so alled sub feature units, wi are te initial parels wit smaller sizes. Wit tese sub feature units, te spetrums, sapes (area, perimeter, et.) and neigbourood topology sould be reorded to serve te following merging proesses. Tese are fulfilled wit Region adjaeny grap (RAG) and nearest neigbour grap (NNG). Figure. RNG & NNG of a parel grap As illustrated in Figure, RAG is an undireted map wi an be expressed as G = (V, E),in wi V={,,,k} is te set of E V * V nodes, E is te set of links, and. Every parel is a node of te map, and a link exists if two nodes are neigbours. Given a speified RAG and its merging ost funtion S, its orresponding NNG an be expressed as G m = (V m, E m ), were V m is just similar to V in RAG, and E m only reords te minimal merging ost of every node, wi indiates NNG is a direted map. In partiular, if te begin and end nodes of a link are superposed, tere exists a yle. NNG improves te merging effiieny tan RAG beause it obviously dereases te storage and alulation of links.. MERGING CRITERION Based on te sub feature units, a merge ost funtion integrating spetral eterogeneity and sape eterogeneity is designed to guide te merging of parels. Te use of sape is to make te merged parels more regular in sapes. Wit experiments, te merging ost funtion is similar to [Baatz et al., 007]: f = w olor + ( w) sape () In wi w is te weigt for spetral eterogeneity falling in te interval [0, ]. A generally suitable weigt for olour is 0.9, and 0. for sape. Too large sape weigt will bring unreasonable segmentation. Te spetral eterogeneity is te variane of te parent parel minus te sum of te varianes of te two ild parels, weigted wit teir respetive areas: olor = w ( nσ ( n + n σ σ )) () were, is te band ount, w is user speified weigts for every band (.0 by default). Te sape eterogeneity is te ombination of ompatness and smootness eterogeneity: sape = wmpt mpt + ( wmpt ) smoot (3) in wi ompatness eterogeneity is alulated as: mpt l l l = n ( n + n ) (4) n n n and smootness eterogeneity is alulated as: smoot l l l = n ( n + n ) (5) b b b were l is te perimeter of a parel, n is te pixels, b is te perimeter of its bounding box. A ommonly suitable setting of w mpt is 0.5. Te merged parel variane an be got wit Formula 6 to avoid redundant alulation: = (( n ) σ + ( n + n n ( m m ) /( n ) σ ) /( n )( n merge ) σ (6) were m,m are te means of te two ild parels. ) 4

3 Te International Arives of te Potogrammetry, Remote Sensing and Spatial Information Sienes. Vol. XXXVII. Part B4. Beijing FAST PARCEL MERGING Wit te initial sub feature units and merging osting funtion, te ommonly used merging semes wi an be adopted inlude te following two kinds: Te main steps of seme one: Step. Input te RAG (NNG) of te initial segmentation; Step. Loop until some merging terminating ondition is satisfied: Selet te link of te minimal merging ost in te RAG (NNG); te orresponding region pair into a new node, and delete te old pair; Update RAG (NNG); Step3. Output te merged RAG (NNG) and terminate te wole merging. Te main steps of seme two: Step. Input te RAG (NNG) of te initial segmenting; Step. Loop until all nodes are merged: Starting from a node, sear its neigbourood nodes. If te merging ost is less tan some tresold, merge tem into a new node until te merging ost of te new node exeeds some speified merging tresold. Exlude tese merged nodes from te following merging; Step3. Output te merged RAG (NNG) and terminate te wole merging. Te advantage of te first merging seme lies in tat it an guarantee te urrent merging pair is te minimal ost one of te un-merged pairs, wi tus indiates tat it is a globe minimal merging ost strategy. But it as severe sortoming of very low effiieny beause te links of a merging node wit all its neigbours sould be rebuilt to find te minimal ost link to update te NNG, wi is a very time-onsuming business. Our experiments indiate tat it isn t suitable for segmenting remotely sensed imagery ommonly wit large data volume and wit te merge ost funtion in setion.. Te seond merging seme needs to visit te parels only one, and ten is of faster speed; but te problems rely in te seletion of merging riterions. If area is seleted as te ontrol fator, it may ause many small parels wit distint spetral differene wit te neigbours to be merged ompulsively. If te merging riterion in setion. is used, it often brings a defet tat sometimes a merge will be too greedy: it often merge too many unsuitable parels beause more merges will oasionally ause smaller merging osts, wi makes te merging unstoppable. Wit a lot of experiments, a quik merging strategy is designed to merge tese sub feature units in a repetitive way. It inludes te following four steps. Step. Input te NNG of te initial segmentation of setion.; Step. Loop until all nodes are merged: Start from node A, find its pointing node B (its minimal merging ost node). A, B if te merging ost of tis node pair is under some speified tresold, and ten reate a new node in te output NNG. If exeeding te tresold, opy node A into te new NNG diretly. In te above merging, if B as been merged before, (for example, into C in te new NNG), ten A will be diretly merged into C and no new nodes will be reated. Step3. Re-build topology for te new NNG; Step4. Redo te above steps on te new NNG if te terminating ondition asn t been reaed; oterwise output te merged RAG (NNG) and omplete te segmentation. Te arateristi of te metod is tat we don t remove a parel from te merging list after it is merged. Tat s to say tat a parel an be merged many times, until all te nodes are visited one. Tis merging strategy avoids te ig onsuming performanes inluding topology rebuilding, merged node searing and deleting, et. We only rebuild te topology one after te entire merging of an image as been aomplised. It s proved tat tis merging strategy doesn t deline te visual feeling of te segmentation, but greatly improves te algoritm effiieny. Just similar to ecognition, a sale parameter is used to ontrol te merging proessing. If all parel merging osts exeed te power of te sale parameter, te wole merge yle breaks and te segmentation is over. Troug experiments, we find tat te minimal merging ost doesn t inrease steadily wit te merging times. It flutuates, wi means namely te latter merging ost sometimes maybe be lower tan te former. But generally it will inrease post after ertain times of merging. Experiments indiate tat totally after 7 to 8 iterations, te wole merging will be terminated. Te sale parameter ontrols te iterating times, wi indiretly ontrols te average size of te parels. Wit anging te sales, a multiresolution segmentation an be realized. 3. EXPERIMENTAL ANALYSIS Our algoritm is implemented wit visual C++ 003, and is tested on te platform of windows XP, wit Pentium 4.93GHz CPU, G memory. Beause image segmentation is only te first step for image information extration, oversegmentation to some degree will not bring serious influenes to sueeding analyses. Keeping tis in mind, te evaluation of metod preision is based on weter a metod well prevents different ground objets from falling into same segmenting parels. Several omparative experiments on different types of images su as SPOT-5, IKONOS wit ecognition 5.0 segmentation module are arried out. To failitate te omparisons, te inputs of our metod and ecognition are unified to te default setting of te latter: w=0.7,w =.0, w mpt =0.5. Figure,, 3 illustrate te experimental results, in wi te left graps are te results of ecogntion, te rigt are of our metods. Table presents te omparisons of te two metods on segmentation preision and effiieny. Wit tese proofs, it an be found tat te two metods give similar and good segmentation in vision, and bot ave teir respetively loal visual worse-or-better segmenting parels. Altoug ecognition generally produes more regular sape parels, it often brings some fragmentized parels distributing around te boundary of many even-tone, large-size parels (see te pond in te upper-left orner of Figure, te river in Figure and te playground in Figure 3). Our metod doesn t ave tis kind of defets yet. In effiieny omparisons, our metod trails ecognition. Maybe tere exist two reasons tat ause te lag: ) wit same sales, our metod peraps merges more times tan ecognition (see te parel number in Table ), wi auses more time onsuming; ) a lot of superfluous time is wasted on our merging steps (for example, te re-alulation of parel topology), wi may be improved wit introduing 5

4 Te International Arives of te Potogrammetry, Remote Sensing and Spatial Information Sienes. Vol. XXXVII. Part B4. Beijing 008 spatial indexes to automatially maintain te topology between parent and ild parels. Parel number Image Time onsuming Our metod ecognition Our metod ecognition SPOT-5 multispetral ( ) About 8s About 8s IKONOS panromati About 65s About 5s Google Eart sreen apture About 58s About 7s Evaluation of segmentation preision Bot metods segment te urban, rivers, ponds, mountains et. orretly, but our metod better keeps te boundary of te ponds. Te segmentations of bot metods are totally similar. ecognition is more regular in sapes, but wit fragmentized parels distributing around te boundary of many eventone, large-size parels. Similar in segmentations. Bot wit some errors. ecogntion is more regular in sapes, but wit te above mentioned fragmentized parels. Table. Comparison of our metod and te multi-resolution segmentation module of ecognition Figure. Segmentation of a SPOT-5 image wit sale 40 Figure. Segmentation of an Ikonos image wit sale 80 6

5 Te International Arives of te Potogrammetry, Remote Sensing and Spatial Information Sienes. Vol. XXXVII. Part B4. Beijing 008 Figure 3. Segmentation of a Google Eart sreen apture image wit sale CONCLUSIONS In tis work, a multi-resolution image segmentation metod ombining spetral and sape features is proposed, wit referene to te basi idea of ecogntion. In most ases, our segmentation metod produes igly visually omogeneous parels in arbitrary resolution on different types of images. We delare tat our metod reaes te level of ecognition in segmenting preision, and satisfies te pratial need of segmenting remotely sensed imagery wit fair algoritm effiieny. As a first step for furter analyses, multiresolution segmentation an be used to produe image objet primitives. Starting from tis, we an arry out a lot of iger level image interpretation inluding image lassifiation, information extration, and objet reognition, et. REFERENCES Aplin P., Atkinson P., Curran P., 999. Per-field Classifiation of Land Use Using te Fortoming Very Fine Resolution Satellite Sensors: Problems and Potential Solutions. In: Advanes in Remote Sensing and GIS Analysis. Ciester: Wiley and Sons In., 9-39 Definiens website, 007. ttp:// (aess ) Baatz M. and Sape A., 007. Multiresolution Segmentationan Optimization Approa for Hig Quality Multi-sale Image Segmentation. ttp:// (aess ) Stoev S. L., 000. RaFSi A Fast Watersed Algoritm Based on Rainfalling Simulation. In Proeedings of 8-t International Conferene on Computer Grapis, Visualization, and Interative Digital Media, Plzen City, Cze Republi Vinent L. and Soille P., 99. Watersed in digital spaes: an effiient algoritm based on immersion simulations. IEEE Trans. Patt. Anal. and Ma. Int., 3(6): Patrik De Smet and Rui Luís V.P.M. Pires, 000. Implementation and analysis of an optimized rainfalling watersed algoritm. Pro.SPIE, 3974: ACKNOWLEDGEMENTS Tis work is supported by Cinese National Programs for Hig Tenology Resear and Development (No.007AAZ4, 006AAZ46), and startup fund of Nanjing Normal University (No XGQ0035) 7

6 Te International Arives of te Potogrammetry, Remote Sensing and Spatial Information Sienes. Vol. XXXVII. Part B4. Beijing 008 8