A COMPARISON BETWEEN DIFFERENT PIXEL-BASED CLASSIFICATION METHODS OVER URBAN AREA USING VERY HIGH RESOLUTION DATA Ebrahim Taherzadeh a, Helmi Z.M. Shafri a, Seyed Hassan Khalifeh Soltani b, Shattri Mansor a and Ravshan Ashurov a a Institute of Advanced Technology (ITMA) and Faculty of Engineering Universiti Putra Malaysia (UPM) 43400 Serdang, Selangor Malaysia b Centre for Environment-Behavior Studies (ce-bs), Faculty of Architecture Planning and Surveying, Universiti Teknologi MARA, Shah Alam, Malaysia. ebrahim.taherzadeh@gmail.com ABSTRACT Cities are centers of human activity and more than half of the world s population live in metropolitan areas. Urban areas are characterized by a large variety of artificial and natural surface materials influencing ecological, climatic and energetic conditions. With advent of new sensors in remote sensing fields that capture the data in high spatial and spectral resolution we are able to classify the urban area accurately. The main goal of this study is, comparison between different pixels based classification methods such as Maximum likelihood, Minimum distance, spectral angel mapper and Support Vector Machine (SVM).Thus to apply these methods we use pansharpening image of worldview 2 from Kuala Lumpur, Malaysia with 0.6 meter spatial resolution and 8 spectral bands. The results show that SVM method more accurate than other methods in classify the urban area with 72% overall accuracy with Kappa coefficient 0.65.However the very high resolution data shows the good potential for classify the urban area but still some limitations exist such as, misclassification between some urban classes such as road and some roof materials, exist of shadows that leads to misclassification of some classes, displacement of high rise building and dense trees and their shadow overlapping the nearby urban features make the mapping more difficult. Presently, we do not have effective and reliable solution to this problem without using additional data. However using pixel based classification method such as SVM is very useful for classify the urban area especially for detection the impervious surface but to avoid the limitations that mentioned above we should use also some spatial and texture information to improve the classification accuracy. Keywords: Urban, Worldview-2, ification, Support vector machine, Spectral-based, Impervious surface INTRODUCTION Extraction of accurate land use and land cover information is one of the most important tasks in remote sensing. Land cover serves as an important source of information for both the scientific and business communities, and remote sensing is a cost-effective method to gather the land-cover information (Teseng et al, 2002). It is obvious that using remote sensing data in heterogeneous area such as urban area the high spatial resolution is needed. Extraction the land cover information for the heterogeneous area such as urban area from the remote sensing imagery can be a difficult task depending on the complexity of the landscape and the spatial and spectral resolution of the imagery being used. Improving the accuracy of land-cover classifications is a fundamental research topic in the field of remote sensing (Song et al, 2005). It is well known that urban area are characterized by large verity of artificial and natural surface materials influencing ecological (Arnold et al, 1996) climatic and energetic (Oke, 1987)conditions. Thus in order to classify the urban area and extract the more reliable information the very high resolution (VHR) imagery, such as Worldview-2 image has been used.while high spatial resolution remote sensing provides more information than coarse resolution imagery for detailed mapping, increasingly finer spatial resolution produces challenges for conventional pixel-based techniques such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and Maximum Likelihood ifier (MLCM) ) (Cleve et al, 2008).In this study try to do the comparison between different supervise spectral-base(pixel-base) classification methods such as Maximum likelihood, Minimum distance, spectral angel Mapper and Support vector machine in order to identify which method is suitable and got the good potential to identify and discriminate between different impervious surface in heterogeneous area.
MATERIAL AND METHOD Study Area The test site was chosen in some part of Kuala Lumpur area, Malaysia, which contains a mixture of historical and modern buildings. The Worldview-2 image which contains 8 spectral bands with 0.68 meter spatial resolution has been used. Worldview-2 image Worldview-2 image Study area in false color Categorization the Urban Materials Based on the Worldview-2 image, the test site was categorized for classification of different materials in the study area. In order to provide the ground-truth image in terms of training and testing data a field survey has been conducted and finally 9 classes were defined (Table1). Table 1. Training and testing pixel es Training Testing water 238 509 grass 763 1530 Polycarbonate roof 165 246 tarmac 201 732 Zinc roof 341 5033 Clay roof 388 9700 Shadow 286 970 Asbestos Roof 244 2204 Cement 399 1890 Preprocessing Before processing the data atmospheric and geometric correction were applied on our images, Due to the lack of some meteorological parameters during the flight using the advance atmospheric correction method is difficult task thus the IAR Reflectance calibration (Internal Average Relative Reflectance) was used to normalize images to a scene average spectrum which is available in ENVI 4.7 software. Processing ification. It aims to label each pixel in a scene to specific land cover types. Different classes in an image can be discriminated with using different classification algorithms using spectral feature. The classification procedures can be supervised or unsupervise.in this study different supervised classification methods were applied on the image such as Minimum distance, Maximum likelihood, spectral angel mapper and SVM which all of them are
spectral-base. Supervised classification requires the training area which is provided by filed survey (figure 1) for all classification method the same training data in 9 classes was used that shows in table 1. Zinc roof Zinc Tarmac Clay Figure 1. Ground-truth image. Minimum Distance It is a statistical approach that finds mean value of pixels of training sets in n-dimensional space and all pixels in image classified according to the class mean to which they are closest. This classification method allow for diagonal boundaries and no overlap of classes. This classification assumes that spectral variability is same in all directions, which is not the case. Maximum Likelihood. It is another statistical approach that assumes multivariate normal distributions of pixels within classes. This classification method builds a discriminate function for each class and for each training class the spectral variance and covariance is calculated.furthermore for each pixel in the image, this function calculates the probability that the pixel is a member of that class. In other hand this classification uses mean and covariance of training data in order to classify the image. Spectral Angle Mapper (SAM). SAM is a physically-based spectral classification and it is an automated method for directly comparing image spectra to known spectra that uses an n-dimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra and treating them as vectors in a space with dimensionality equal to the number of bands. Support Vector Machine. Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies.svm can work well with a small training data set as the selection of a sufficient number of pure training pixels has always been a problem with remote sensing data. Assessment. Generally, classification accuracy refers to the extent of correspondence between the remotely sensed data and reference information (Congalton, 1991). In order to assess the accuracy of land cover maps extracted from the Worldview-2 image, the testing data which is provided based on the field survey (table 1) were used and the results were recorded in a confusion matrix. A non-parametric Kappa test was also used to measure the classification accuracy. RESULT AND DISCUSSION After supervise classification of the image with different classification methods the figure 2. Shows the minimum distance classification result with overall accuracy 44.15% and Kappa coefficient 0.33. Table2 Shows the classification accuracy of each class.
Table 2. classification accuracy of minimum distance classification Figure 2. Minimum distance classification result. Water 100 40.61 cement 30.11 8.69 polycarbonate 24.45 12.23 Asbestos 62.07 26.89 Zinc 14.68 71.54 Clay 44.79 88.98 tarmac 66.12 27.82 Vegetation 91.06 99.15 Shadows 98.45 90.69 accuracy 44.15 Figure 3. Shows the maximum likelihood classification result with overall accuracy 51% and Kappa coefficient 0.40.table 3shows the classification accuracy for each class. Table 3. classification accuracy Maximum likelihood Figure 3. Maximum Likelihood classification result Water 100 40.61 cement 72.96 17.42 polycarbonate 25.18 19.88 Asbestos 62.07 26.89 Zinc 0 0 Clay 67.86 97.03 tarmac 70.49 32.85 Vegetation 100 25.13 Shadows 95.26 99.78 accuracy 52 Figure 4. Shows the classification result after applying the SAM classification, the result shows that the maximum accuracy 43% with Kappa coefficient 0.34.
Table 4. classification accuracy SAM Figure 4. spectral Angle Mapper classification result. Water 100 40.61 cement 40.85 18.69 polycarbonate 22.26 7.04 Asbestos 51.5 58.44 Zinc 0 3.55 Clay 67.86 97.03 tarmac 85.11 24.67 Vegetation 90.09 99.89 Shadows 99.59 98.98 accuracy 43.4 The final result related the SVM classification with the overall accuracy 72%. Table shows the classification for each class. Table 5. classification accuracy SAM Figure 5. Support Vector machine classification result. Water 100 81.41 cement 64.39 40.26 polycarbonate 24.82 77 Asbestos 80.85 35.53 Zinc 68.81 99 Clay 71.10 98.08 tarmac 67.21 44.40 Vegetation 100 93.12 Shadows 98 80 accuracy 72 Base on the Minimum distance classification result some impervious surface such as the Clay and Asbestos roof detected better than other roof materials in this scene. The Cement did not detect well compare to the other classification methods. Some misclassification appears in this type of classification such as Clay and Asbestos roof, Cement vs. Clay and Tarmac vs. Cement. Regarding the maximum likelihood classification this result is better and more accurate(approximately 10%) than Minimum distance classification.in this type of classification almost the classes detected better than previous classification such as the clay roof,cement and Tarmac detected better
compare to the minimum distance classification method. But still a lot of misclassification exists between different classes especially between different roof materials. Regarding to the SAM classification result with the overall accuracy is 43% and the Kappa coefficient 0.34 shows a lot of misclassification exists in this type of classification method only the shadows area detected nicely but for detection the impervious surface is not suitable.the final classification method that used in this study is one of the advance supervise classification method which is the SVM.the result of this classification method show that this is more accurate compare to the other classification method which were used in this study with overall accuracy 72% and 0.65 Kappa coefficient.most of the classes especially impervious surface detected much more better than others methods. CONCLUSION In this study different supervise classification such as Minimum distance, Maximum likelihood, Spectral angle Mapper (SAM) and support vector machine (SVM) methods were applied on Worldview-2 image that was taken from the Kuala Lumpur area in order to make the comparison between different spectral-based classification method to explore the capability of them to extract the impervious surface. The result shows that the SVM classification method shows the good potential and result to extract the impervious surface from the heterogonous area and much more accurate around 20 %-30% more than other classification method,but still some misclassification exist in this type of classification which is related to the spectral similarity between different classes, illumination direction and building geometry. Furthermore the Worldview-2 image shows the good potential in order to discriminate the urban materials. But to achieve the more accurate result it should be combined the spatial and texture information which is inherent in the Worldview-2 image and using the spectral information is not sufficient for urban remote sensing when the more detail of information is needed. REFERENCES Arnold C. L. J. and C. J. Gibbons,1996.Impervious Surface Coverage: The emergence of a key environmental indicator, Journal of the American Planning Association,62(2):243 258. Cleve, C., M. Kelly.,F.R. Kearns,2008. ification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography, Computers Environment and Urban Systems, 32(4): 317-326. Civco, D., and J. D Hurd,2005. A competitive pixel-object approach for land cover classification, International Journal of Remote Sensing, 26(22):4981-499. Congalton, R.G.,1991. A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, 37(1):35 46. Oke, T.R. 1987, Boundary layer climates (2 Edn.), New York, Methuen and Co. Ltd., Routledge. Song, M., D. L. Tseng, M.H., Chen, S.J., Hwang, G.H and Shen M.Y,2002. A genetic algorithm rule-based approach for land-cover classification, ISPRS Journal of Photogrammetry and Remote Sensing, 63(2):202-212.