Evaluation of the Pixel Based and Object Based Classification Methods For Monitoring Of Agricultural Land Cover Case study: Biddinghuizen - The Netherlands Hossein Vahidi MSc Student of Geoinformatics - ITC and KNTU Joint Program, Faculty of Geo-Information Science and Earth Observation (ITC), the University of Twente,Enschede, The Netherlands, vahidi23800@itc.nl ABSTRACT Many researches have been done to find a suitable method to classify the remote sensing data. Traditional classification methods are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Instead of pixels, pixel groups and object based methods offer the suitable approaches to classify remote sensing data. In this research the maximum likelihood as a pixel based method and object based classification method are used to classify remote sensing data from an agricultural region in Biddinghuizen -The Netherlands.Also the results of this study are evaluated and compared. KEY WORDS: Pixel Based, Maximum Likelihood, Object Based, Classification 1- Introduction The remotely sensed data automatic classification is an essential action in the process of generating or updating GIS databases. Pixel based image classification as a conventional land cover classification method classifies images by considering the spectral similarities with the predefined land cover classes. Although the techniques are well developed and have sophisticated variations such as soft classifiers, sub-pixel classifiers and spectral un-mixing techniques, it is argued that it does not make use of the spatial concept (Blaschke et al. 2001). For example Zhou (2001) claimed that Maximum likelihood classifier (MLC) was limited by utilizing spectral information only without considering contexture information. And texture information was ultimately necessary if one is to obtain accurate image classifications. These shortages of classical pixel based methods were made researchers to work on objects instead of single pixels and the concept of object based classification was founded. This paper is concerned with evaluation of pixel based and object based methods and a comparison of the outputs of these methods. The following section introduces the study area of the research. The third section will talk about the data which is used in the case study. The forth section discusses about maximum likelihood classification method and evaluation of the results of case study. In the section five, object based classification method is introduced and the results of this method are evaluated. Finally, the end of this paper is reserved for final comments and directions. 2- Study area The area of interest for this study is located in Biddinghuizen region. This area represents a typical agricultural region in the Netherlands. Biddinghuizen is a modern agricultural area in Oostelijk Flevoland, one of the former Ijsselmeer Lake polders (Abkar,1994).The agricultural fields are large and usually rectangular. The main crops are grass, potatoes, cereals, sugar beets, beans, peas, and onion. The specific color was assigned for each crop in ILWIS
(Figure1). The elevation differences in the Biddinghuizen region are very small. This region is a well known area that we have a good set of data and information about it (Abkar, 1994). FIG 1.The main crops in the study area 3-Data The RS data that is used for experiment is a Landsat Thematic Mapper (TM) image that was acquired on 7 July 1987. The image was of good quality and no atmospheric corrections were performed. The image was georeferenced to the national triangulation system using a first-degree affine transformation. The pixels were resampled to the original size of 30 m by 30 m (Abkar, 1994). A large number of studies of agricultural areas show that one optimal band combination to study agricultural areas consist of a visible, a near infrared and middle infrared band (Abkar, 1994). Therefore, in this experiment bands 3, 4, and 5 of TM image were also used; these indeed are the least correlated bands (Figure 2).Also the GIS pre-ready land cover map of this area (DB87) is used in some stage of this research (Figure 3). FIG 2. Landsat Thematic Mapper (TM) image (bands 3,4,5), Biddinghuizen region - The Netherlands,1987 FIG 3. Land cover map of study area based on DB87 4- Maximum likelihood classification method Maximum likelihood classification (MLC) is the most common supervised pixel based classification method used with remote sensing image data (Richards et al, 2006). In MLC method it is assumed that the spectral values of training pixels and the statistics for each class in each band are normally distributed and MLC calculates the probability that a given pixel belongs to a specific class. Unless you select a probability threshold, all pixels are classified. Each pixel is assigned to the class that has the highest probability (that is, the maximum likelihood). If the highest probability is smaller than a threshold you specify, the pixel remains unclassified (Richards et al, 2006).In the following sections we will discuss about the results of classification of the study area by MLC method. 4-1- Training pixels Figure 4 show the distribution of training pixels and Table 1 manifests the sample statistics for each class per band such as mean, standard deviation and total number of sample
pixels. As it is illustrated in this table, there are unsuitable great values for standard deviation of some sample pixels in some or all bands. This can be caused by various types of one especial crop or different age of them or human errors in assigning the sample pixel to the specific class. So in the case one and two, sometimes it is preferred to divide one class of general crop to two or more specific classes. FIG 4. Training pixels distribution in the study area Class1: Table 1. Sample statistics for the study area class 5: Band Mean StDev Nr Pred Total Band Mean StDev Nr Pred Total 1:00 24.2 7 61 21 222 1:00 46 13.7 31 23 252 2:00 134.6 22 18 147 222 2:00 80.3 30.7 36 59 252 3:00 67.9 8.5 29 67 222 3:00 82 16.6 23 86 252 Class2: class6: Band Mean StDev Nr Pred Total Band Mean StDev Nr Pred Total 1:00 21.4 0.8 140 21 291 1:00 25.2 3 53 23 260 2:00 107.8 6.5 29 106 291 2:00 144.6 18.9 20 150 260 3:00 48.1 3.4 38 50 291 3:00 56.1 5.9 42 58 260 class3: class7: Band Mean StDev Nr Pred Total Band Mean StDev Nr Pred Total 1:00 23.5 1.1 105 23 272 1:00 35.4 7.4 23 37 211 2:00 158.1 7.1 27 159 272 2:00 88.5 21.5 22 79 211 3:00 87.8 3.1 42 86 272 3:00 63.1 12.1 13 67 211 class4: Band Mean StDev Nr Pred Total 1:00 24.1 2.4 71 23 254 2:00 114.7 7.5 31 118 254 3:00 50.4 3.7 43 50 254
4-1-1 Spectral separability analysis Seven classes exist in the study area. The feature space diagrams show that there are overlapping between classes in all the combinations of the spectral feature space. The best way to measure how well the classes can be Separated is to use separability analysis methods for classes such as: Euclidian distance, Divergence, Transformed divergence, Jeffries- Matusita method (Richards et al, 2006). Training pixels were exported from ILWIS to ENVI in the vector format. The calculations were implemented in ENVI 4.4 and Jeffries-Matusita separability values are reported (Table 2). These values range from 0 to 2.0 and indicate how well the selected sample pairs are statistically separate(richards et al, 2006). Values greater than 1.9 indicate that the sample pairs have good separability(richards et al, 2006). For sample pairs with lower separability values, we should attempt to improve the separability by editing the samples or by selecting new samples. For sample pairs with very low separability values (less than 1), we might want to combine them into a single sample. In this study the current samples is accepted as proper samples and future studies is based on these training pixels. Table 2. Spectral separability analysis.notice: The Layer s number is as the same as class s number but the colors of classes are not as the same as what were defined in ILWIS. (Jeffries-Matusita) Pair Separation (least to most); EVF: Layer: 1.dxf [White] 228 points and EVF: Layer: 7.dxf [Magenta] 211 points - 1.13842405 EVF: Layer: 5.dxf [Yellow] 265 points and EVF: Layer: 7.dxf [Magenta] 211 points - 1.18095626 EVF: Layer: 1.dxf [White] 228 points and EVF: Layer: 6.dxf [Cyan] 260 points - 1.34676553 EVF: Layer: 1.dxf [White] 228 points and EVF: Layer: 5.dxf [Yellow] 265 points - 1.35844890 EVF: Layer: 2.dxf [Red] 285 points and EVF: Layer: 4.dxf [Blue] 252 points - 1.49188198 EVF: Layer: 4.dxf [Blue] 252 points and EVF: Layer: 7.dxf [Magenta] 211 points - 1.57831386 EVF: Layer: 5.dxf [Yellow] 265 points and EVF: Layer: 6.dxf [Cyan] 260 points - 1.64143388 EVF: Layer: 4.dxf [Blue] 252 points and EVF: Layer: 5.dxf [Yellow] 265 points - 1.65076953 EVF: Layer: 6.dxf [Cyan] 260 points and EVF: Layer: 7.dxf [Magenta] 211 points - 1.68705268 EVF: Layer: 4.dxf [Blue] 252 points and EVF: Layer: 6.dxf [Cyan] 260 points - 1.72926377 EVF: Layer: 1.dxf [White] 228 points and EVF: Layer: 4.dxf [Blue] 252 points - 1.75634300 EVF: Layer: 2.dxf [Red] 285 points and EVF: Layer: 7.dxf [Magenta] 211 points - 1.83228650 EVF: Layer: 1.dxf [White] 228 points and EVF: Layer: 3.dxf [Green] 277 points - 1.88269934 EVF: Layer: 2.dxf [Red] 285 points and EVF: Layer: 5.dxf [Yellow] 265 points - 1.88807367 EVF: Layer: 1.dxf [White] 228 points and EVF: Layer: 2.dxf [Red] 285 points - 1.90511510 EVF: Layer: 3.dxf [Green] 277 points and EVF: Layer: 7.dxf [Magenta] 211 points - 1.96637048 EVF: Layer: 2.dxf [Red] 285 points and EVF: Layer: 6.dxf [Cyan] 260 points - 1.97196243 EVF: Layer: 3.dxf [Green] 277 points and EVF: Layer: 6.dxf [Cyan] 260 points - 1.99671787 EVF: Layer: 3.dxf [Green] 277 points and EVF: Layer: 5.dxf [Yellow] 265 points - 1.99997441 EVF: Layer: 2.dxf [Red] 285 points and EVF: Layer: 3.dxf [Green] 277 points - 1.99999999 EVF: Layer: 3.dxf [Green] 277 points and EVF: Layer: 4.dxf [Blue] 252 points - 2.00000000 4-2- Evaluation of MLC results No image classification is said to be complete unless its accuracy has been assessed. To determine the accuracy of classification, a sample of testing pixels is selected on the classified image and their class identity is compared with the reference data (ground truth). In this study different thresholds for MLC method were implemented in ILWIS and the overall accuracy of them were calculated (Chart 1). Although the basic results show that the best overall accuracy happens in the threshold 0.5, this threshold is not produce an effective and legible classification output with clear boundaries (Figure 5). As the Chart 2 shows the number of wrong classified pixels in this threshold are zero but according to the Chart 3 about 99.95 % of pixels in study area are not classified in this threshold.so it is obvious that the overall accuracy alone, is not a very accurate criterion, and sometimes it can tells a lie to an
0 amateur users and mangers. The empirical study shows that this problem will be solved in the thresholds which are around and more than 2.5 so we concentrate to find an optimum threshold in this range. By weighting to 3 parameters of: overall accuracy, percentage of unclassified pixels and spatial distribution of the errors (by an operator s experience), we choose the optimum threshold. So after the consideration of these three parameters threshold 5 is chosen in this study. Figure 6 depicts the maximum likelihood classification of the study area based on threshold 5.Also Figure 7 shows the spatial distribution of errors in the study area which we must pay attention to them beside overall accuracy parameter. As it is clear most of our errors are happened in the boundaries and mixed pixels which we will try to solve this problem by the mean of object based classification in the next sections. O v e r a l l A c c u r a c y - T h r e s h o l d O v e r a l l A c c u r a c y 9 0 8 0 7 0 6 0 5 0 4 0 3 0 2 0 1 0 7 8. 6 7 4. 6 T h r e 0. 5 T h r e 1 6 7. 9 7 6 4. 1 6 5. 2 6 6. 2 5 6 6. 1 5 6 5. 6 6 5. 4 1 6 5. 2 3 6 5. 2 2 6 5. 2 2 6 5. 2 2 6 5. 2 2 6 5. 2 2 T h r e 1. 5 T h r e 2 T h r e 2. 5 T h r e 3 T h r e 4 T h r e 5 T h r e s h o l d T h r e 6 T h r e 9 T h r e 1 0 T h r e 1 5 T h r e 2 5 T h r e 5 0 T h r e 1 0 0 Chart 1. Overall accuracy of the study area by a ML classification per different thresholds Chart 2. Number of wrong classified pixels by ML method per threshold
0 p e r c e n t a g e o f u n c l a s s i f i e d p i x e l s 1 2 0 P e r c e n t a g e o f U n c l a s s i f i e d p i x e l s o u t o f t o t a l p i x e l s 1 0 0 8 0 6 0 4 0 2 0 9 9. 9 5 9 7. 1 3 8 4. 4 2 3 6. 7 9 2 3. 9 9 1 6. 9 9. 4 3. 8 1. 6 6 0. 3 3 0. 2 1 0 0 0 0 T h r e 0. 5 T h r e 1 T h r e 1. 5 T h r e 2 T h r e 2. 5 T h r e 3 T h r e 4 T h r e 5 T h r e 6 T h r e 9 T h r e 1 0 T h r e 1 5 T h r e 2 5 T h r e 5 0 T h r e 1 0 0 T h r e s h o l d Chart 3. Percentage of unclassified pixels by ML method per threshold FIG 5. Out put of the study area classification based on ML method by the threshold 0.5 FIG 6. Out put of the study area classification based on ML method by the threshold 5 FIG 7. Spatial distribution of error in the study area
0 5- Object Based method It was recognized that traditional pixel-based image analysis is limited because of the following reasons: image pixels are not true geographical objects and the pixel topology is limited; pixel based image analysis largely neglects the spatial photo-interpretive elements such as texture, context, and shape; the increased variability implicit within high spatial resolution imagery confuses traditional pixel-based classifiers resulting in lower classification accuracies (Hay and Castilla 2006).In order to solve some of these natural pixel-based problem,the concept of object based classification as an alternative to pixel based analysis was introduced in 1970s. 5-1- Implementation In this case the results of MLC method with different thresholds and the boundaries of the classes which were derived from the database (DB87) were implemented in ILWIS and the object based classification outputs of the study area were produced. At the end the overall accuracy of them were calculated. Before manifesting of this research results it must be mentioned that the accuracy of segmentation directly influences the performance of object oriented image classification. Only good segmentation results can lead to object oriented image classification out performing pixel-based classification. So It is assumed that the DB87 data are quit accurate and the segmentation method which is chosen is completely reliable. The other assumption in this method is that the majority of the pixels within an object have been correctly classified in a per pixel classification. A part from this it is assumed that field geometry is known (contained in a GIS) and that only one crop is grown in each field. 5-2- Evaluation of results In the object based method, the number of classified pixels from each class (which it is derived from MLC output) is calculated in each segment.after that 7 values for 7 classes are produced. In the other word we calculate the most occurring (predominant) class for each object.at the end the predominant class in each segment determines the class label of that segment. As it is obvious in the chart 4, we have a low overall accuracy in the threshold 0.5. This can be explained by this fact that, the absence of classified pixels in classified MLC image in some areas (segments) are caused to an object based algorithm can t classify some segments (Figure 8). So all of the pixels of these segments are remain unclassified and this causes to decrease the overall accuracy. O v e r a l l A c c u r a c y - T h r e s h o l d O v e r a l l A c c u r a c y 1 0 0 9 0 8 0 7 0 6 0 5 0 4 0 3 0 2 0 1 0 9 1. 6 6 9 2. 4 6 9 2. 0 3 9 1. 9 8 9 1. 9 8 8 7. 0 2 7 5. 9 7 5. 8 5 0. 5 1 2 3 5 6 5 0 1 0 0 T h r e s h o l d Chart 4. Overall accuracy of the study area by an object classification per different thresholds
FIG 8. Overlay of field segments and output of MLC in the threshold 0.5 - Unclassified segments in the threshold 0.5 in the object based classification of study area As Chart 4 illustrates we have considerable increase from threshold 0.5 to 2.It is because of the fact that in the MLC layer there are more pixels which are classified in the study area.so the probability of existence of classified pixel in the segment is increased. As a result the numbers of unclassified segments are reduced and the overall accuracy grows. In threshold 3 we don t have any unclassified segment, but as we see we have a better overall accuracy in threshold 5 because we have more classified pixels (by MLC method) in each segment. In the other word more classified pixels are concern with our calculations. So as a result we have more realistic condition and the natural rule of homogeneity of neighbor pixels in each crop field are more implemented. However the best accuracy is happened in threshold 5 which it is 92.46 and after that we have decrease in the overall accuracy because of existence of more wrong classified pixels (in corresponded MLC) in the segments (Figure 9). Figure 10 shows the spatial distribution of the errors and as it is illustrated there are less error in boundaries in the comparison with pixel based classification. It can be interpreted by this fact that the object based classification algorithm works on objects instead of pixels. In the other word an advantage of object based classification is that mixed pixels and spectral variability have only little effect on the classification result. The main reasons for incorrect classification by an object based method can be explained by spectral confusion, incorrect field geometry, errors in DB87 as a ground truth and fields for which the ground resolution of TM is too low (size/shape). Also it can be seen that we have more errors in small segments in the object based method because of the origin of this method.this method is based on assignment of the predominant class to whole pixel of that segment. So as we mentioned before if we have more classified pixels in the calculation of predominant class in each segment we have more realistic calculation and as it is obvious this fact will be happened more in lager segments (which it happens more in the bigger segments).
FIG 9. Out put of the study area classification based on the object based method by the threshold 5 FIG 10. Spatial distribution of error in the study area 6- Comparison of the results of the pixel based and object based classification Chart 5 shows the result of overall accuracy in each threshold for pixel based and object based method. As it is illustrates there are sharp increase in the object based overall accuracy values.this comparison is manifested that after the threshold 2, which we don t have any unclassified segment, the overall accuracy of object based method is considerably better than pixel based method.so as a result it could be said that the geometry data can improve remote sensing classification accuracy. Chart 5. Comparison of the overall accuracy in each threshold for the ML and object based method 7- Conclusion The main error sources in traditional image analysis methods are: material properties effects, similarity in spectral properties (radiometric overlap), adjacent pixels and size of
objects relative to the sensor pixel size. As it was shown in this study we have many problems and errors in the training and classification stages of MLC, especially in the boundaries of fields. These shortages of classical pixel based methods were considerably reduced by the usage of the object data which are mostly stored in a GIS data bases can improve significantly the result and overall accuracy of the classification projects. This method is very efficient especially in the cases like agricultural farms which the boundaries of fields are known. So in these cases, by the application of suitable threshold which was classified all of the segments it is expected to have sharp improvement in overall accuracy of the results in the comparison with pixel based MLC method. Finally, it is advised that the optimum threshold in MLC method which was selected base on 3 parameters of overall accuracy, percentage of unclassified pixels and spatial distribution of the errors will be calculated again by the application of model based classification method and the result will be compared with the outcome of this research. Acknowledgment I would especially like to thank Dr.Ali Abkar for his valuable advice and suggestions and providing the dataset. References 1. Abkar, A.A., 1994: Knowledge-Based Classification Method for Crop Inventory Using High Resolution Satellite Data, MSc. Thesis, ITC, Enschede, The Netherlands. 2. Richards, J. A., X. Jia,, 2004,Remote Sensing Digital Image Analysis, 4th Edition, Springer. 3. Weih R., N.Riggan, 2oo8, A Comparison of Pixel-based versus Object-based Land Use/Land Cover Classification Methodologies, Elsevier. 4. Linli C., S.Jun, 2008, Comparison Study on the Pixel-based and Object-oriented Methods of Land-use/cover Classification with TM Data, International Workshop on Earth Observation and Remote Sensing Applications. 5. Volker W., 2004, Object-based classification of remote sensing data for change detection, ISPRS Journal of Photogrammetry & Remote Sensing. 6. Blaschke, T., J.Strobl, 2001, what s wrong with pixels? Some recent development interfacing remote sensing and GIS, GeoBIT/GIS, 6, 12-17. 7. Hay, G.J., G.Gastilla, 2006, Object-based image analysis: strength, weakness, opportunities, and threats (SWOT), 1st International Conference on Object-based Image Analysis (OBIA 2006), 4-5, Salzburg, Austria.