An Evaluation of an Object-Oriented Paradigm for Land Use...

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An Evaluation of an Object-Oriented Paradigm for Land

Use/Land Cover Classification*

Rutherford V. Platt and Lauren RapozaGettysburg College

Object-oriented image classification has tremendous potential to improve classification accuracies of land useand land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aimto quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixedurban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared atraditional pixel-based classification using maximum likelihood to the object-oriented image classificationparadigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at leastfour components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearestneighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluatedeach of these components individually to determine the source of any improvement in classification accuracy.We found that the combination of segmentation into image objects, the nearest neighbor classifier, andintegration of expert knowledge yields substantially improved classification accuracy for the scene comparedto a traditional pixel-based method. However, with the exception of feature space optimization, little or noimprovement in classification accuracy is achieved by each of these strategies individually. Key Words: imageclassification, land cover, land use, object-oriented.

La clasificacion de imagenes orientadas a objetos tiene un enorme potencial para mejorar la precision en laclasificacion de los usos y coberturas del suelo (land use and land cover, LULC); sin embargo, sus beneficiossolo se han probado mınimamente en estudios revisados por expertos en el campo. Nuestro objetivo escuantificar los beneficios de un metodo orientado a objetos en comparacion con un metodo tradicional basadoen pixeles en la region mixta urbana-suburbana-agrıcola que circunda a Gettysburg, Pensilvania. Para hacerlo,comparamos la clasificacion tradicional basada en pixeles usando la maxima probabilidad con el paradigmade la clasificacion por imagenes orientadas a objetos integrada en el programa eCognition Professional 4.0.Este paradigma orientado a objetos tiene al menos cuatro componentes que tıpicamente no se usan en laclasificacion basada en pixeles: (1) el procedimiento de segmentacion, (2) el clasificador por el vecino mascercano, (3) la integracion de conocimiento experto, y (4) la optimizacion del espacio de trabajo. Evaluamosindividualmente cada uno de estos componentes para determinar la fuente de cualquier mejora en la precisionde la clasificacion. Encontramos que la combinacion de la segmentacion en objetos imagenes, el clasificador

∗Thanks to Gettysburg College for supporting this research through a Research and Professional Development Grant.

The Professional Geographer, 60(1) 2008, pages 87–100 C© Copyright 2008 by Association of American Geographers.Initial submission, August 2006; revised submissions, May and June 2007; final acceptance, June 2007.

Published by Taylor & Francis, LLC.

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88 Volume 60, Number 1, February 2008

por el vecino mas cercano y la integracion de conocimiento experto dan como resultado una precision en laclasificacion substancialmente mejorada para la escena cuando se compara con el metodo basado en pixeles.Sin embargo, con excepcion de la optimizacion del espacio de trabajo, se logra poca o ninguna mejora en laprecision de la clasificacion con cada una de estas estrategias implementadas de manera individual. Palabrasclave: clasificacion de imagenes, cobertura del suelo, uso del suelo, orientado a objetos.

O bject-oriented image classification at itssimplest level is the classification of ho-

mogeneous image primitives, or objects, ratherthan individual pixels. Object-oriented imageclassification has numerous potential advan-tages. If carefully derived, image objects areclosely related to real-world objects. Oncethese objects are derived, topological relation-ships with other objects (e.g., adjacent to,contains, is contained by, etc.), statistical sum-maries of spectral and textural values, and shapecharacteristics can all be employed in the clas-sification procedures (Benz et al. 2004). De-spite many advances, object-oriented methodsare still computationally intensive and the im-provement in classification accuracy over tradi-tional methods is not always clear.

The idea of object-based image analysishas been around since the early 1970s (deKok, Schneider, and Ammer 1999), but im-plementation lagged due to lack of computingpower. On a limited basis, specialized object-oriented software packages were employed inthe 1980s to extract roads and other linear fea-tures (McKeown 1988; Quegan et al. 1988).These methods of analysis were difficult to em-ploy and inefficient compared to pixel-basedmethods, which began to employ advancedtechniques such as fuzzy sets, neural networks,and textural measurements. Since the 1990s,computing power has increased and high spa-tial resolution imagery has become common,prompting a new emphasis on object-orientedtechniques (Franklin et al. 2003).

Recently, object-oriented image classifica-tion has been successfully used to identifylogging and other forest management activi-ties using Landsat ETM+ imagery (Flanders,Hall-Beyeer, and Pereverzoff 2003); to mapshrub encroachment using QuickBird imagery(Laliberte et al. 2004); to quantify landscapestructure using imagery from Landsat ETM+,QuickBird, and aerial photography (Ivits et al.2005); to map fuel types using Landsat TMand Ikonos imagery (Giakoumakis, Gitas, andSan-Miguel 2002); and to detect changes inland use from imagery in the German national

topographic and cartographic database (Walter2004).

Few direct comparisons of object-orientedand traditional pixel-based methods havebeen published, and these primarily appear inconference proceedings. One study found thatobject-oriented methods yield similar classifi-cation accuracy to traditional methods, but thatsegmentation of pixels into objects makes theclassification more “map-like” by reducing thenumber of small disconnected patches (Will-hauck 2000). Another study compared fourchange detection methods: traditional post-classification, cross-correlation analysis, neuralnetworks, and an object-oriented method(Civco et al. 2002). The study found that therewas no single best way to perform change anal-ysis, and suggested that future object-orientedmethods based on “multitemporal objects”could improve on current results. A third studycompared pixel-based and object-oriented landuse classification for a scene in the Black Searegion of Turkey and found that the object-oriented classification method had a substantialadvantage over the pixel-based parallelepiped,minimum distance, and maximum-likelihoodclassifiers (Oruc, Marangoz, and Buyuksalih2004). A comparison of land cover classi-fication in northern Australia found thatobject-oriented classification yielded improvedclassification accuracy, 78 percent versus 69.1percent (Whiteside and Ahmad 2005). Overall,the literature suggests either that object-oriented methods have a real advantage or thatthey demonstrate little advantage but muchpromise.

These studies all use various versions ofthe eCognition software (recently renamedDefiniens), a specialized image classificationsoftware package that integrates hierarchicalobject-oriented image classification, fuzzylogic, and other strategies to improve classifi-cation accuracy. At the time of this research,eCognition Professional 4.0 was the most fullydeveloped object-oriented classification soft-ware available. The object-oriented paradigmin eCognition has at least four components

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Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification 89

not typically used in traditional pixel-basedclassification methods: (1) the segmentationprocedure, (2) nearest neighbor classifier,(3) the integration of expert knowledge,and (4) feature space optimization. Weevaluate the object-oriented paradigm ineCognition for classification of an urban–suburban–agricultural landscape surroundingGettysburg, Pennsylvania.

Our research questions are as follows: First,to what extent does the eCognition object-oriented paradigm increase land use/land cover(LULC) classification accuracy over a tradi-tional pixel-based method for this scene? Sec-ond, how much of the increased accuracy,if any, is due to segmentation of image ob-jects, the classifier, expert knowledge, or fea-ture space optimization? This study is distinctfrom previous studies because it independentlyevaluates these four elements of the eCognitionobject-oriented paradigm to determine their ef-fect on classification accuracy.

Methods

Study Area and DataThe study area is 148 km2 in size, and lo-cated in a rural area northwest of the Balti-more and Washington, DC, metropolitan ar-eas (Figure 1). It encompasses the borough ofGettysburg, the Gettysburg National MilitaryPark, and surrounding areas. The area is lo-cated in the southwest corner of the Newark-Gettysburg basin, which is situated betweenthe South Mountain (an extension of the BlueRidge Mountains) to the west and the Susque-hanna River to the east. The area comprises ex-tensive agriculture divided by narrow woodedbands and, increasingly, low-density residen-tial development. We acquired a georectifiedIKONOS satellite image of the study area taken25 July 2003. The image has a spatial resolu-tion of 4 m and has four spectral bands: blue(480.3 nm), green (550.7 nm), red (664.8 nm),and near infrared (805.0 nm).

Classification ModelsThe image was classified into seven LULCclasses: forest, fallow, water, recreationalgrasses, commercial/industrial/transportation,cultivated, and residential (Table 1). Theseclasses represent the most common and impor-

tant land uses and covers in the area. We did notuse a more detailed classification scheme suchas the one used for the National Land Coverdata set (NLCD; Vogelmann et al. 2001) be-cause many of the NLCD classes are missingfrom the area (e.g., ice/snow, shrublands), rare(e.g., high-density development), or not distin-guishable using a single date image. Two to fourimage objects were selected as training samplesfor each class for use in the classification pro-cedures described in the next section.

A total of eight image classifications wereconstructed to compare the object-orientedparadigm to a traditional classification (Table2). Model 1 represents the best object-orientedmodel, model 2 represents the traditional pixel-based classification, and models 3 through 8fall somewhere in between. We compared pairsof models that differed in one key respect; ei-ther in terms of analysis level (pixel or ob-ject), classifier (maximum likelihood or nearestneighbor), expert knowledge (used or not), orfeature space optimization (used or not). Theseterms and the specific differences betweenmodels are discussed in the following sections.

Analysis Level: Pixel versus ObjectTraditional image classification methods clas-sify individual pixels, whereas object-orientedclassification methods classify homogeneousregions, or image objects. The process of ag-gregating pixels into image objects is known asimage segmentation. For the models operatingat the object level, the image used in this studywas segmented using the fractal net evolutionapproach (FNEA), which is a multifractal ap-proach implemented in the eCognition imageprocessing software (Baatz and Schaepe 2000;Baatz et al. 2004). FNEA is a pairwise clusteringprocess that finds areas of minimum spectraland spatial heterogeneity given a set of scale,color, and shape parameters (Benz et al. 2004).

The size of the image objects is determinedby the scale parameter, a unitless number re-lated to the image resolution that describesthe maximum allowable heterogeneity of imageobjects. As the scale parameter increases, thesize of the image objects also increases (Benzet al. 2004). The color and shape parametersare weights between zero and one that deter-mine the contribution of spectral heterogeneity(in this case red, green, blue, and near infrared)

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Figure 1 Study area.

and shape to the overall heterogeneity that isto be minimized. The smoothness and com-pactness parameters are additional weights be-tween zero and one that determine how shape iscalculated.

Spectral heterogeneity is defined as the sumof standard deviations of each image band.Minimizing only spectral heterogeneity resultsin objects that are spectrally similar, but thatmight have fractally shaped borders or many

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Table 1 Land use/land cover classes

Class Description

Forest Closed canopy forestFallow Field with little or no vegetationWater Stream, lake, or pondRecreational grasses Mowed grass in a suburban or urban context, might have scattered treesCommercial/industrial/transportation Parking lots, industrial sites, strip malls, and associated infrastructureCultivated Cropland, pasture, and unmowed grassesResidential Single or multifamily housing

branched segments (Baatz et al. 2004). To ad-dress this issue, the segmentation process canalso incorporate shape in terms of compactnessor smoothness. Compactness is defined as theratio of the border length and the square root ofthe number of object pixels. Smoothness is de-fined as the ratio of the border length and theshortest possible border length derived fromthe bounding box of an image object (Baatzet al. 2004).

The “best” compactness and smoothness pa-rameters depend on the size and types of ob-jects to be extracted. For example, an objectrepresenting an agricultural field would ide-ally have high smoothness and compactness,whereas an object representing a riparian areaalong a stream would ideally have low smooth-ness and compactness.

It is important to note that there is nosuch thing as optimal parameters for imagesegmentation (Benz et al. 2004). For object-level models, the normal procedure is simplyto iteratively try different parameters untilthe resulting objects are appropriately sizedand shaped for the particular task. Aftertesting many possible parameters, we usedthe following: scale: 50, color: 0.7, shape:0.3, smoothness: 0.5, compactness: 0.5. Theresulting objects closely corresponded to theboundaries of fields, woodlots, strip malls, and

other elements of interest in the image (Fig-ure 2). For pixel-level models, we segmentedthe image into single-pixel objects.

Image ClassifierThe models are divided according to what clas-sifier they use: maximum likelihood or near-est neighbor. The maximum likelihood clas-sifier calculates the probability that a pixelor object belongs to each class and then as-signs the pixel or object to the class withthe highest probability (Richards 1999). It isone of the most commonly used classifiers be-cause of its simplicity and robustness (Platt andGoetz 2004). The Environment for Visualiz-ing Images (ENVI) image processing softwarewas used for maximum likelihood classification.The nearest neighbor classifier is a part of theeCognition object-oriented paradigm and as-signs each object to the class closest to it infeature space.

Expert KnowledgeThe models are also divided according towhether or not they employ expert knowledge(i.e., user-developed classification rules) inaddition to the classifier (nearest neighborclassifier). Traditional classification methodstypically do not employ expert knowledge,whereas the eCognition object-oriented

Table 2 Summary of classification models

Analysis Expert FeatureModel level Classifier knowledge space

1 Object Nearest neighbor Yes Spectral2 Pixel Maximum likelihood No Spectral3 Object Nearest neighbor No Spectral4 Pixel Nearest neighbor No Spectral5 Object Maximum likelihood No Spectral6 Pixel Nearest neighbor Yes Spectral7 Object Nearest neighbor No Optimized8 Pixel Nearest neighbor No Optimized

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Figure 2 Before and after segmentation.

paradigm integrates membership functionswith the nearest neighbor classifier. Member-ship functions allow objects to be classifiedusing simple rules related to spectral, shape,and textural characteristics or on relationshipsbetween neighboring objects. For example,we might observe that recreational grasses aretypically surrounded by developed areas likecommercial/industrial/transportation or resi-dential. Knowing this, a membership functioncan be constructed that assigns each object avalue between zero and one depending on thepercentage of that object that is surrounded byan object defined as developed (Figure 3). Foreach object, the lowest of the two probabilities(the membership function probability and thenearest neighbor probability) is selected as theactual probability of belonging to a class. Thecloser this number is to one, the more likelythe object will be classified as recreationalgrasses. For models that use expert knowledge,membership functions were defined for everyclass except for water (Table 3).

Feature Space OptimizationFinally, models were divided into those that usefeature space optimization and those that donot. Typically classification is conducted us-ing the spectral or textural bands chosen bythe analyst. In the feature space optimizationprocedure, the user first specifies which fea-ture space bands should be included in the

analysis. For objects, the feature space bandsmight number in the hundreds and includespectral data, hierarchical data, shape data, andtextural data. For pixels, the feature space isconsiderably smaller and is limited to spectraland textural data. Feature space optimizationthen calculates all the bands and sorts thembased on how well they separate the classes.

Figure 3 Example of a membership function.

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Table 3 Membership functions

Class Factors promoting membership Factors limiting membership

Commercial/industrial/transportation — Relative border to residentialRecreational grasses Relative border to residential or

commercial/industrial/transportation

Relative border to residential orcommercial/industrial/transportation

Residential Adjacent to recreational grasses,low homogeneity

Relative border to commer-cial/industrial/transportation,cultivated, or fallow

Cultivated Rectangular fit Relative border to residentialFallow Rectangular fit Relative border to residentialForested — Relative border to residentialWater — —

The thirty feature space bands that best sep-arated the LULC classes were used to clas-sify the image for models that used featurespace optimization. The bands beyond thesethirty did little to help separate classes and sowere not used in the classification. Texturaldata were computed following Haralick, Shan-mugam, and Dinstein (1973). The grey levelco-occurrence matrix describes how differentcombinations of pixel values occur within anobject (Baatz et al. 2004). The grey level differ-ence vector, another way to measure texture,is the sum of the diagonals of the grey levelco-occurrence matrix.

At the object level (model 7), the followingsix bands best separated the classes accordingto the feature space optimization:

� Grey level co-occurrence matrix correla-tion of band 3 (red). This measures thecorrelation between the values of neigh-boring pixels in the red band.

� Shape index: perimeter divided by fourtimes the square root of the area of anobject.

� Degree of skeleton branching: describesthe highest order of branching of skele-tons, defined as lines that connect themidpoints of a Delaunay triangulation ofan object. High values indicate a complexgeometrical structure of the object (Benzet al. 2004).

� Maximum pixel value of band 4 (near in-frared).

� Grey level difference vector entropy ofband 3 (red). This measures whether pix-els have similar brightness levels in the redband.

� Grey level co-occurrence matrix entropyof band 1 (blue). This measures whetherpixels have similar brightness levels in theblue band.

At the object level (model 8), the followingsix bands best separated the classes accordingto the feature space optimization:

� Grey level co-occurrence matrix mean ofband 1 (blue). This measures the meanfrequency of pixel values in combinationwith neighboring pixel values in the blueband.

� Grey level co-occurrence matrix contrastof band 4 (near infrared). This measuresthe amount of variation in the near in-frared band within an object.

� Grey level co-occurrence matrix varianceof band 4 (near infrared). Similar to greylevel co-occurrence matrix contrast, thismeasures the dispersion of variation in thenear infrared band within an object.

� Grey level co-occurrence matrix mean ofband 4 (near infrared). This measures themean frequency of pixel values in combi-nation with neighboring pixel values in thenear infrared band.

� Mean difference to neighbor, band 4 (nearinfrared). This measures the mean differ-ence between a pixel value and its neighborin the near infrared band.

� Grey level co-occurrence matrix homo-geneity of band 1 (blue). This measuresdegree that the object displays a lack ofvariation in the blue band.

One possible issue with feature space op-timization is overfitting—fitting a statisticalmodel with too many parameters such that the

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Table 4 Confusion matrix, model 1

Model

Analysis

level Classifier

Expert

knowledge

Feature

space

Average

user’s

acc.

Average

prod.

acc.

Weighted

agreement

Weighted

disagree

location

Weighted

disagree

quantity

1 Object Nearest neighbor Yes Spectral 72% 71% 78% 13% 9%

CIT Cultivated Fallow Forested

Recreational

grass Residential Water Total

Prod.

acc.

CIT 94 0 4 1 0 8 0 107 88%Cultivated 3 101 2 18 12 3 0 139 73%Fallow 14 1 14 2 0 7 0 38 37%Forested 0 4 1 85 6 5 0 101 84%Recreational grass 3 18 0 7 54 17 0 99 55%Residential 8 0 2 1 0 23 0 34 68%Water 1 0 0 0 0 2 28 31 90%Total 123 124 23 114 72 65 28 549 71%User’s acc. 76% 81% 61% 75% 75% 35% 100% 72%

Note: CIT = Commercial/industrial/transportation.

model fits the training data much better thanthe validation data. To minimize overfitting,we excluded bands with nonnormalized unitsof length or area; objects of any size can belongto any class.

Classification EvaluationTo evaluate classification accuracy of the eightmodels, a random sample of 300 points wasgenerated across the image in areas that are nottraining sites. This random sample was usedto determine the relative proportion of eachclass within the image. Following Congaltonand Green (1999), the random sample was sup-plemented by a stratified random sample of 250points. This ensured that each class in the clas-sified image contained at least thirty validationpoints. All 550 validation points were overlainon the original imagery and on 0.6 m georecti-fied aerial photography taken in spring 2003.An image analyst identified the LULC classat the object level and the pixel level, alwaysdeferring to the IKONOS imagery for classesthat might have changed between the times thetwo images were taken. A second image analystrevisited any points that were tagged as “ques-tionable” by the first analyst. If the two analystsdisagreed, the point was visited in the field bythe first analyst to make a final determinationof the class. A total of thirty-five points werevisited in the field.

Using all 550 points, a confusion matrix wasgenerated to compare the predicted LULCclasses to the actual LULC classes. The diago-nal of the matrix shows the number of objectsor pixels where the predicted class is the same

as the actual class, whereas the off-diagonal val-ues show the number of objects or pixels wherethe predicted class is different from the actualclass. For each model, user’s accuracy (proba-bility that a site in the classified image actuallyrepresents that class on the ground) and pro-ducer’s accuracies (probability that a site on theground was classified correctly) were calculatedfor each class. The average user’s accuracy, av-erage producer’s accuracy, and weighted agree-ment (percent correct weighted by the actualoccurrence in the landscape, as estimated by the300-point random sample) were also reported.

Note that our accuracy reporting does notinclude Kappa, which attempts to correct forchance agreement, because this measure isdifficult to interpret, has an arbitrary definitionof chance agreement, and conflates differentsources of error (Pontius 2000). Instead, wereported weighted disagreement due to loca-tion (percentage of objects or pixels incorrectlyclassified because the predicted location wasincorrect) and weighted disagreement dueto quantity (percentage of objects or pixelsincorrectly classified because the predictedquantities of classes were incorrect). Bothpercentages are weighted by prevalence of theclasses in the image.

Results and Discussion

Representing the eCognition object-orientedparadigm, model 1 (Table 4) operates onthe object level, uses the nearest neighborclassifier, incorporates expert knowledge, anduses only the spectral feature space. Theweighted agreement (percent correct weighted

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by prevalence of the class) was 78 percent, thehighest of all the models (Table 4). The user’saccuracy was over 70 percent for all classes ex-cept fallow and residential. Because residentialis a rare class that is spectrally similar to manyother classes, areas known to be residentialwere only 68 percent likely to be classifiedcorrectly. Producer’s accuracy was over 70percent for all classes except fallow, residential,and recreational grasses. Sites known to befallow were misclassified 37 percent of thetime because of the spectral similarity to com-mercial/industrial/transportation. The classesof commercial/industrial/transportation, culti-vated, forested, and water were over 70 percentaccurate from both the user’s and producer’sperspective. The weighted disagreement dueto location is 13 percent and the weighted dis-agreement due to quantity is 9 percent, showingthat neither type of error predominates.

Representing pixel-based image classifica-tion, model 2 operates on the pixel level, usesthe maximum likelihood classifier, does not in-corporate expert knowledge, and uses spectralfeature space (Table 5). The weighted agree-ment is 64 percent, which is 14 percent lowerthan model 1. Model 2 has a lower classificationaccuracy than model 1 for every class except fal-low, which has a higher user’s and producer’saccuracy than model 1. Whereas weighted dis-agreement due to quantity is similar betweenmodel 1 and 2 (9 percent versus 12 percent),the weighted disagreement due to location isclearly superior in model 1 compared to model2 (13 percent versus 24 percent).

Analysis Level: Pixel versus ObjectTo evaluate the effect of analysis level we com-pared model 3 (Table 6) to model 4 (Table 7).Both models use the nearest neighbor classifier,omit expert knowledge, and use spectral featurespace, but model 3 operates at the object levelwhereas model 4 operates at the pixel level.The weighted agreement is similar for bothmodels—60 percent for model 3 and 61 per-cent for model 4. Weighted disagreement dueto location is identical (17 percent) and similarfor weighted disagreement due to quantity (23percent versus 22 percent). The accuracy withinclasses varies, however. These results suggestthat classifying objects rather than pixels doesnot necessarily improve classification accuracy.

ClassifierTo evaluate the effect of the classifier wecompared model 3 (Table 6) to model 5(Table 8). Both models operate at the objectlevel, omit expert knowledge, and use spectralfeature space. They differ only in terms of theclassifier: model 3 uses the nearest neighborclassifier, whereas model 5 uses the maximumlikelihood classifier. Model 3 has a lowerweighted agreement than model 5 (60 percentversus 71 percent). Model 3 also has a similarweighted disagreement due to location (17 per-cent versus 18 percent) and a higher weighteddisagreement due to quantity (23 percent ver-sus 11 percent). This suggests that maximumlikelihood does a better job in predicting thequantity of classes, but is comparable to nearest


Model

Analysis

level Classifier

Expert

knowledge

Feature

space

Average

user’s

acc.

Average

prod.

acc.

Weighted

agreement

Weighted

disagree

location

Weighted

disagree

quantity

2 Pixel Maximum likelihood No Spectral 63% 53% 64% 24% 12%


Recreational


Prod.

acc.



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Model

Analysis

level Classifier

Expert

knowledge

Feature

space

Average

user’s

acc.

Average

prod.

acc.

Weighted

agreement

Weighted

disagree

location

Weighted

disagree

quantity

3 Object Nearest neighbor No Spectral 62% 61% 60% 17% 23%


Recreational


Prod.

acc.



neighbor in predicting the location. However,because several less common classes (water andresidential) are poorly classified in model 5, theaverage producer’s accuracy for this model (58percent) is lower than for model 3 (61 percent).

Expert KnowledgeTo evaluate the effect of expert knowledge in-tegrated into membership functions, we com-pared model 1 (Table 4) to model 3 (Table 6)and model 6 (Table 9) to model 4 (Table 7).Within each pair, the models differ only interms of whether they use membership func-tions in addition to the nearest neighbor clas-sifier (models 1 and 6 do, models 3 and 4 donot). The first pair of models operates at theobject level and the second set operates at thepixel level.

At the object level, membership functionsimprove classification accuracy considerably.Model 1 is higher than model 3 in terms of

weighted agreement (78 percent versus 60percent), average user’s accuracy (72 percentversus 62 percent), and average producer’saccuracy (71 percent versus 61 percent). Inparticular, cultivated, residential, and recre-ational grasses are improved from both theuser’s and producer’s perspective when mem-bership functions are used. The classificationaccuracy of water remains the same becausemembership functions are not used in eithermodel. Model 1 is superior to model 3 interms of weighted agreement due to location(13 percent versus 17 percent) and quantity(9 percent versus 23 percent). This shows thatmembership rules especially help predict thequantity of objects in each class.

At the pixel level, there is no improvementin classification accuracy when membershipfunctions are used. Models 4 and 6 are similarin weighted agreement (61 percent versus 60percent). Model 6 has a higher average user’s


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4 Pixel Nearest neighbor No Spectral 60% 62% 61% 17% 22%


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accuracy than model 4 (73 percent versus 60percent), but a lower average user’s accuracy(60 percent versus 62 percent). An importantdifference between the two models is theweighted disagreement due to location andquantity. For model 6, the weighted disagree-ment due to location is 7 percent, whereasthe weighted disagreement due to quantity is33 percent. Classes like residential and recre-ational grasses are severely underpredicted bymodel 6. In contrast, for model 4, the weighteddisagreement due to location is 17 percent,whereas the weighted disagreement due toquantity is 22 percent. Compared to model 6,model 4 does a better job predicting the numberof pixels in each class, but a worse job predict-ing the locations of these pixels. Overall theresults show that these membership functionsdo not improve classification at the pixel level.

Feature Space OptimizationTo evaluate the effect of feature space opti-mization, we compared model 3 (Table 6) tomodel 7 (Table 10) and model 4 (Table 7) tomodel 8 (Table 11). The first pair operates onthe object level, whereas the second pair op-erates on the pixel level. Models 3 and 7 usespectral feature space only, whereas models 7and 8 use feature space optimization. Featurespace optimization benefits classification accu-racy at both the object and pixel level. At theobject level, model 7 has a weighted agree-ment of 71 percent, whereas model 3 has aweighted agreement of 60 percent. At the pixellevel, model 8 has a weighted agreement of67 percent, whereas model 4 has a weightedagreement of 61 percent. In neither case do themodels outperform model 1, which uses expertknowledge but not feature space optimization.


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user’s

acc.

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prod.

acc.

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agreement

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Weighted

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6 Pixel Nearest neighbor Yes Spectral 73% 60% 60% 7% 33%


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Model

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7 Object Nearest neighbor No Optimized 57% 56% 71% 20% 9%


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LimitationsThis analysis has several limitations. First, theresults should not automatically be extendedto other contexts; they are partially a functionof the spatial and spectral resolution of the im-age, the composition of the scene, the classifica-tion system, and the segmentation parameters.However, the results are consistent with thegrowing body of literature that shows an advan-tage for object-oriented classification methods(Willhauck 2000; Oruc, Marangoz, and Buyuk-salih 2004; Whiteside and Ahmad 2005). Sec-ond, although we tested several aspects of theobject-oriented image classification paradigmin eCognition, we cannot be sure that we max-imized the software’s potential. For example,the segmentation parameters and membershipfunctions we used are not necessarily optimal.

A third limitation is that, despite the sim-ple classification system, many of the classes

might be difficult to distinguish spectrally. Partof the potential difficulty in separating theseLULC classes is due to the spatial and spectralresolution of the imagery. High spatial reso-lution imagery, like the imagery used in thisstudy, can be difficult to classify due to the highspectral heterogeneity within classes (Wood-cock and Strahler 1987; Donnay 1999; Lalib-erte et al. 2004). Furthermore, multispectralIKONOS imagery contains only four spectralbands and thus has poorer spectral resolutionthan many other common multispectral sensorssuch as Landsat 7.

Conclusions

This study started with two research ques-tions: (1) to what extent object-oriented imageclassification increases LULC classification ac-curacy over a traditional pixel-based method


Model

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8 Pixel Nearest neighbor No Optimized 59% 63% 67% 22% 11%


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for this scene, and (2) how much of the in-creased accuracy, if any, is due to segmenta-tion, the classifier, expert knowledge, or fea-ture space optimization. The answer to thefirst question is clear: we found that the object-oriented paradigm implemented in eCognitionyields a considerable improvement in classifi-cation accuracy over a traditional method forthis scene. Weighted agreement of the bestobject-oriented method (model 1) was 78 per-cent, which is 14 percent higher than the modelrepresenting the traditional pixel-based classifi-cation (model 2). That said, the object-orientedparadigm is no magic bullet: when classes over-lap spectrally as in this study, high classificationaccuracy is still difficult to achieve.

The answer to the second question is thatthe combination of the object analysis level, thenearest neighbor classifier, and expert knowl-edge yields the highest classification accuracy.Therefore, for the most part, we cannot at-tribute the advantage of the object-orientedparadigm to any one of these methods in isola-tion. For example, we found that the classifica-tion of image objects rather than pixels by itselfyields no increase in weighted agreement (∼60percent weighted agreement in each case formodels 3 and 4). Furthermore, using the near-est neighbor classifier rather than the maximumlikelihood classifier actually decreases weightedagreement (60 percent for model 3, 71 percentfor model 5) and does a worse job predictingthe quantity of classes (weighted disagreementof 23 percent for model 3 and 14 percent formodel 5).

When paired with a nearest neighbor classi-fier, membership functions yielded a large im-provement in classification accuracy at the ob-ject level (weighted agreement of 78 percent formodel 1 compared to 60 percent for model 3).At the pixel level, there is no improvement inclassification when membership functions areused (weighted agreement of ∼60 percent forboth models 4 and 6). It is important to un-derscore membership functions are not exclu-sive to object-oriented classification; they canbe implemented for pixel-based classificationas well. In this study, most of the member-ship functions are related to relative border andadjacency, which can be applied at both thepixel and object level. However, membershipfunctions are more broadly applicable at theobject level because certain variables commonlyused in membership functions, such as shape,

are only meaningful for objects. Feature spaceoptimization led to an increase in classifica-tion accuracy at both the object and pixel level,but feature space optimization models still per-formed poorer than model 1, which integratedexpert knowledge.

The study shows that, for this image and clas-sification system, the object-oriented paradigmimproves classification accuracy considerably.Unlike previous studies, we investigated thesource of this advantage and found that muchof the benefit is derived from the ability tointegrate expert knowledge through member-ship functions, which is only effective at theobject level. To simplify the comparison withtraditional image classification methods, weused a flat classification scheme and a singlelevel of image objects. A next step in the studywill be to develop a nested hierarchy of imageobjects and classify land cover at each levelusing information from subobjects (e.g., trees,grass) and superobjects (e.g., city park, forest).This would reduce direct comparability withpixel-based methods, but allow us to takefull advantage of multiresolution capabilitiesof eCognition and potentially further boostclassification accuracy. �

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RUTHERFORD V. PLATT is an Assistant Profes-sor in the Department of Environmental Studies atGettysburg College, Gettysburg, PA 17325. E-mail:[email protected]. His research interests includespatial models of human–environment interactionsand emerging methods in remote sensing.

LAUREN RAPOZA is a graduate of GettysburgCollege and is currently an outreach specialistat the Maryland Science Center. E-mail: [email protected].

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