Classification trees for improving the accuracy of land use urban data from remotely sensed images

Transcription

1 Classification trees for improving the accuracy of land use urban data from remotely sensed images M.T. Shalaby & A.A. Darwish Informatics Institute of IT, School of Computer Science and IT, University of Nottingham, UK. Abstract This paper summarizes the results of a joint research carried out between the Informatics Institute of IT, University of Nottingham in the UK and the Information Technology Institute (ITI) in Egypt. The research focuses on achieving higher classification accuracy to extract urban planning data from remotely sensed images. The research aims at providing an affordable system, which is capable of operating with limited data to produce the best possible classification accuracy. A Landsat TM scene of Greater Cairo Metropolitan Area (GCMA) is used to test the validity and reliability of the proposed classification tree. The paper includes three main sections. The first introduces the current problem of the accuracy of urban data and the proposed solution. The second discusses the target classes, the most commonly used classification techniques and the proposed classification tree. The last section compares the experiment results and highlights the research conclusions. Findings show that the proposed classification tree yielded the highest accuracy for both the 'Overall Classification Accuracy' (OA) and the 'Kappa Statistic' (K^). However, the process of constructing the tree was complicated, and required a greater level of user intervention. Results show that remotely sensed images could be used as an alternative to traditional surveying methods to provide up-to-date urban data with a high level of accuracy.

2 382 Management Information Systems 1. Introduction 1.1. Problem Definition During the last fifty years, urban growth in many countries has witnessed a large increase. This increase is more significant in developing countries, particularly in Delhi, Mexico City, and Cairo. Such exploitation, in large metropolitan areas, represents a real threat to both man-made and natural environments. This threat has urged regional planners and politicians to act. However, this requires welldefined management plans that are built on up-to-date and accurate information. Currently there are a number of tools that can be utilized in producing and updating land-use maps, e.g. Aerial Photography, Global Positioning Systems (GPS) and Surveyors. Depending on such tools has become inefficient and inadequate due to the continuous and rapid urban growth. It is estimated that it would take several years to update Cairo's land-use maps, by which time, the collected data would have been out of date, moreover such a task is costly. On the other hand, easy access to low cost and efficient computer hardware and software with the variety in the sources of remotely sensed data has presented digital images as a good alternative for traditional surveying techniques. To make use of such data, accurate and reliable image processing techniques have to be available. This research examines the efficiency of combining 'Decision trees' together with image processing technology to obtain a thematic map. The objective is to provide a simple, efficient, and affordable system, capable of operating with limited land-use data, that would produce an acceptable classification accuracy in extracting urban land-use data from a Landsat TM scene of Greater Cairo Image Classification To extract the required information, remotely sensed images have to be classified, which is defined by Cortijo et. al. [3] as obtaining a thematic map from a multi-spectral image. Lillesand and Kiefer (1994) stressed that as with other image processing techniques, e.g. rectification, and image enhancement, image classifiers may be used in a hybrid mode. Also, there is no single "right" manner in which to approach an image classification problem. However, the particular approach one might take depends upon the nature of the data being analyzed, the computational resources available, and the intended application of the classified data. Techniques used in classifying remotely sensed data can be broadly categorized into two types; unsupervised and supervised. Unsupervised classifiers, where the data is analyzed independently according to a statistically based decision rule, are computer automated and so they require little or almost no human interaction or interpretation. The most known example is ISODATA. Commonly used supervised classifiers are also statistically based, e.g. Minimum Distance (MD) which calculates the spectral distance between the candidate

3 Management Information Systems 383 pixel's value and the mean value of the signature, and Maximum Likelihood (ML) which is based on the probability that a pixel belongs to a particular class. Other supervised classifiers include Decision Trees, AI & Expert Systems that are based on logic classification theories, and integration of data from the remotely sensed images with socio-economic data. All supervised classifiers require greater level of human interaction or interpretation to fit with the data specifications (Avery & Berlin, 1992) Methodology This research is carried out in four main stages, target classes identification, data training, classification, and accuracy assessment. 1. The target classes are selected based on previous knowledge of the investigated site and older land use maps. 2. An essential step in the classification process is the training step where the computer is trained to recognize different patterns in the data. (Flygare, 1997) 3. Once a set of reliable signatures has been created, the following step is to carry out the classification process. In this research five classification techniques are tested, MD, ML, PAR, USC and the proposed classification tree. 4. After performing the classification process, an approach is needed to assess the accuracy of its output. There are several accuracy assessment approaches; some are visual while most of them are statistically based. 2. Case Study 2.1. Greater Cairo Metropolitan Area Cairo city, Egypt's capital, is the main part of Greater Cairo Metropolitan Area (GCMA) and is formed of three Governorates. GCMA is deemed the largest in Africa and the Middle East with respect to population and size. GCMA has a total population of more than 11 million people, as of 1/1/1997. (CAPMAS, [2]) and a total inhabited area of square kilometre. The continuous increase in population and informal development has resulted in the severe damage of GCMA's environment and urban fabric. Among the problems associated with such development are deficiencies in infrastructure; conflict in land-use; insufficient road and highway networks and inadequate housing supply. (Ministry of Housing Study, [1]) 2.2. Data Sets The data used to test the different classification techniques is a sub-scene (22 x 2312 pixels) retrieved from a Landsat TM image of GCMA. (Fig. 1) The scene was acquired on 17/12/1988 with a -cloud cover. The sub-scene has 256 grey levels and the actual size of each pixel is 3 x 3m, except for band 6, the thermal band, where the actual size of each pixel is 12 x 12m.

4 384 Management Information Systems Figure 1: Cairo Sub-Scene (4, 3, 2) The dark coloured pixels represent the urban areas, the light coloured pixels represent the desert, the moderate colour pixels represent the vegetated areas and the black areas represent the water bodies. 3. Training Data Training is an initial step in the classification process regardless of the adopted technique. The result of the training step is a set of signatures that defines a training sample or cluster. Each signature corresponds to a class, and is used with a decision rule to assign the pixels in the image file to a class. One or more of the following methods can be used by the user to identify training samples: using a vector layer, using a class from a thematic layer, identifying seed pixels with similar spectral characteristics and defining a polygon. The last two methods are utilized in this research. In this research, the extraction of the training information is carried out in two steps, collecting the training areas and then merging them. Training areas are collected using the 'Seed Properties' tool based on the diagonal neighbourhood option and under the following constraints: maximum area of 4 pixels, and a spectral Euclidean distance of 1 units. The exceptions for this were the 'Desert' class training areas, which were selected by picking 148 AOIs. This is due to the fact that it is very easy to separate the bare soil pixels from other classes and because of the large adjacent areas of desert. This step produced 758 training areas containing 177,783 pixels. (Table 1) The reason behind the large number of training areas is to account for the interclass spectral variance, during the classification process. Due to the same reason,

5 Management Information Systems 385 the training areas are spatially distributed all over the image. The second step was to merge these preliminary training areas to from the five target classes. Using this data the traditional classifiers were trained and the tree was constructed. Table 1: Number of Training Areas, Pixels, and Inter Class Seperability Class Name Desert Vegetation Water Urban Roads Total = # of Preliminary Training Areas # q/tow Training Pixels Separability Avg. Min Classification Accuracy Accuracy is defined as the ability to extrapolate successfully from the training areas to the whole mapped area and statistically it is the probability that at least (I) pixels are correctly classified, when a random sample of (N) pixels is selected. Consequently, in order to evaluate the accuracy of maps derived from remotely sensed data one has to compare the digitally classified image to samples obtained from field observations, maps or airphotos. A combination of all possibilities is always recommended. Picking an approach is highly dependent on the available computational resources, money, data and time. Two statistical techniques are used in this research, the Error Matrix (EM), and Kappa statistic (k^). The former reports three accuracy measures, Overall Accuracy (OA), Producer's Accuracy (PA), and User's Accuracy (UA). A major drawback of ER and its three accuracy measures is that they do not contain information about off-diagonal cell values. Normalized overall accuracy, e.g. (K^) accounts for this issue and consequently it is sometimes argued that it is a better representation of the classification accuracy. (Gonzalez and Woods, [6]) As explained by Gonzalez and Woods [6], due to the large number of pixels in remotely sensed data, traditional thinking about sampling does not apply. Consequently, a balance between what is statistically sound and what is practicably attainable must be found. Most analysts, prefer Stratified Random (SR) sampling by which a minimum number of samples are selected from each class to ensure that all the classes, even those with a small spatial presence, are efficiently represented. In this research 724 pixels were randomly generated using the SR technique as the distribution parameter with a minimum of 5 pixels for each class.

6 386 Management Information Systems 5. Decision Trees Friedl and Brodley [5] define a decision tree as a classification procedure that recursively partitions a data set into smaller subdivisions on the basis of a set of tests defined at each branch in the tree. They also stressed that decision trees have substantial advantages for remote sensing classification problems because of their flexibility, intuitive simplicity, computational efficiency, and being better suited to handle non-normal, non-homogeneous data sets. Hansen et al [7] added that any hierarchical natures of independent variables are explicit with trees, which is of tremendous help, because unknown relationships between predictor and predicted variables may be discovered. On the other hand, Mather (1987) explains that their disadvantages include being scene specific, less automated, labour intensive and being dependent on highly trained labour. 5.1 Tree Construction There are many methods to construct the tree and all have a well-established base in statistical analysis. The most common are hierarchical clustering and splitting algorithm. (Ru-Ye, [13]). The former is adopted in this research due to its relative simplicity. Ru-Ye [12] defines the hierarchical clustering guiding principle as classifying sequentially according to similarity or the between-class distances among classes as the distance between any two classes in the feature space directly reflects the similarity between them. The method is a bottom-up procedure. The procedure first considers the K-classes as K-clusters in the feature space, and then gradually merges them to form, at last, one big group containing all the K-clusters. In this procedure, the two clusters with the smallest between-class distance are merged. Each operation of merging two classes and/or groups results in a new group. After (K-l) operations of this kind, K- clusters become a big group and the tree structure is obtained. (Fig. 2 & 3) The Evaluate Signature Separability tool, present in the software, was used to calculate the Euclidean Distance (ED), as a representative of the between-class distance. Table 2 shows that the Roads and Water classes have the minimum ED, 24. Thus they are left to be separated at the bottom of the tree. Figure 2: Example of a Sequented Merging Procedure Figure 3: Example of a Tree Structure

7 Table 2: \* Level of Euclidean Distance Management Information Systems 387 Vegetation Urban Desert Water Roads Vegetation Urban Desert Water 24 Roads The 2 * level of ED showed that the Wat-Road group and Vegetation class have the minimum ED, 45. Consequently they are separated just above the Urban and Road classes. Using the previous results and results from the 3"* and 4* levels of ED, the tree is constructed. Figure 4. Illustrates that the Desert class is the first to be separated from the whole data set, followed by the 'Urban' class, the Vegetation class and finally the Roads and Water classes are separated from each other Decision Rules Analysing the signature mean plot diagram (SMPD) and the display histogram window it was possible to estimate the decision rules at each node of the tree. From the SMPD, it was very clear that B5 is the most appropriate band for separating the Desert class from the other four classes. Still to achieve better accuracy it is always desirable to involve more than one band in each decision rule. The best results are achieved by subtracting Bl from B5, and the conditional equation: B5 - Bl > The Urban class decision rule involved six bands using the following equations: 98 < Bl > 18 and 36 < B2 > 95 and 46 < B3 > 141 and 37 < #4 > 74 a/wnj < BJ > 776 and22 < B6 > 77 The Vegetation class is separated using the NDVI: B4 - B3 / B4 + B3 > Finally the Road class is separated from the Water class according to the following equations: B4-B3 < and B5 > 5 The tree was constructed based on the previous decision rules and using the model maker, available in the software. (Figure 5) C3 Figure 4: The proposed classification tree Figure 5: The Developed Model

8 388 Management Information Systems 5.3. Classification Results Table 3 reveals that the best classification accuracy is not the same in the Error Matrix (EM) and the Kappa Statistic (K^). While, the Desert class achieved a production accuracy of 91.52% and a users' accuracy of 87.92% in the Error Matrix, the Urban class had the best (K^) statistic of.852. Table 3: Proposed Tree (a) Error Matrix (b) K/\ Statistic Tagpt Qass Ds. Ete. Uh \%s Road \\& Tctel RtxIAE. (% lih Veg Road m Total CXoaUOassificaticnAxiracy^ 772% Uas Axx M & Tag* Class Ds lib \% ftxd CKoall Kappa Statistics = Traditional Classifiers: Supervised and Unsupervised As explained before four other classifiers, then the classification tree, are tested in this research USC The USC is executed using the ISODATA model. The number of the output classes was chosen to be 96 and then they were merged to form the five target classes. The convergence threshold was.97 and the maximum number of iterations was MD As described by Lillesand and Kiefer [9] the MD strategy is mathematically simple and computationally efficient, but with certain limitations. The most significant limitation is its insensitivity to different degrees of variance in the spectral response data. Table 4 shows that the MD classifier, with values of

9 Management Information Systems % for PA and 87.11% for UA and.7556 for (K*), achieved the best results in classifying the desert areas ML The ML classifier quantitatively evaluates both the variance and covariance of the category spectral response patterns when classifying an unknown pixel. To do this an assumption is made that the distribution of the cloud of points forming the category training data is Gaussian. In essence the ML classifier delineates ellipsoidal Equiprobability Contours in the scatter diagram. The shape of the equiprobability contours express the sensitivity of the likelihood classifier to covariance. (Lillesand and Kiefer, [9]) Table 4 shows that, once again, the Desert class achieved the best classification accuracy in both the EM & (K^) Statistic with a producer's accuracy of and a users' accuracy of 85.22% while the (K*) statistic is PAR Management Information Systems, C.A. Brebbia & P. Pascolo (Editors) A common problem in the classification process is the category variance, which can be tackled by considering the range of values in each category of the training set. However, difficulties are encountered when category ranges overlap. The resulting problems can be somewhat encountered by using the PAR classifier. This modification was added to the absolute MD & ML classifiers. Table 4 shows that while the introduction of the PAR rule increased significantly the 'Urban' class PA from 6.16% to 72.66, it decreased the Roads class PA from 5.% to 13.33%. However, it can be argued that it had a positive effect on the achieved PA accuracies. On contradiction to the PA, the UA has decreased. Table 4 shows that the (K^) for the Urban class has increased and the overall (K^) has increased from.5137 to Similar to all the previous classifiers the Desert class achieved the best PA, UA, and (K*). 7. Comparison & Conclusion Table 4 shows that the proposed tree has the best possible UA for the Desert, Urban and Water classes, with values of 87.92%, 83.96, and 27.27% respectively. The ML-Par has the best UA for the Roads class and finally the MD has the best possible UA value for the Vegetation class. Concerning the PA, table 4 reveals that the proposed classification tree has the best possible PA for the Urban, Vegetation and Roads classes, 69.53%, 81.82% and 43.33% respectively. While the MD-Par has the best possible classification value for the urban class, and the ML and ML-Par have the best possible values for both the Desert and the Water classes respectively. To summarize, the proposed tree achieved either the best PA or the best UA for each individual class. Concerning the overall accuracy, table 4 shows that the

10 39 Management Information Systems proposed tree has the best possible overall accuracy, 77.21%, followed by the ML, while the worst is the MD. Table 4: Comparison of Classification Accuracy Class Desert Urban Vegetat -ion Road Water OA K/\ Ace. PA% UA% K^ PA% UA% K/\ PA% UA% K^ PA% UA% K^ PA% UA% K^ use MD ML MD-P ML-P Tree 91, , ,96, , Referring to the (K^) statistic, again the proposed tree has the best possible (K^) for three individual classes, Desert, Urban, and Water, with values of.77,.81 and.21 respectively. And finally the proposed tree has the best overall (K*) of.6619 followed by the ML, while the worst is the MD. Based on the above results the following conclusions are drawn out: 1. Achieving a better 7% overall accuracy than the ML, the classification tree arises as a good alternative for traditional classification techniques. The same reason compensates for the longer times spent in constructing the tree compared to the automated classifiers. 2. The USC proved to be ineffective in our case as it had the worst accuracies in almost all of the individual classes and the overall classification accuracy. 3. The introduction of the PAR had a negative effect on the classification accuracies; thus it is better not to use it. 4. Although the ML came the second in both the OA and the overall (K^) it did not achieve high classification accuracies in any of the individual classes accuracies except for the PA in the Desert class. The USC did not achieve very good accuracy neither at the single class level nor the overall accuracy.

11 Management Information Systems It is acceptable to use remotely sensed images as an alternative to traditional surveying methods as they overcome the problem of data updating. References 1. Avery, T. E., & Berlin, G. L., Fundamentals of Remote Sensing & Airphoto Interpretation, Fifth Edition, Macmillan Publishing Company, New York, CAPMAS, Census Report, Cairo, Egypt, Cortijo, F. J., and De La Blanca, N. P., 'Improving Classical Contextual Classifications',/^. J. of Remote Sensing, 19, , Flygare, A., 'A Comparison of contextual classification methods using Landsat TM', Int. J. of Remote Sensing, 18, , Friedl, M. A., & Brodley, C. E., 'Decision Tree Classification of Land Cover from Remotely Sensed Data', Remote Sensing of Environment, 61, , Gonzalez, R. C., Woods, R. C., Digital Image Processing, Third Edition, Hansen, M., Dubayah, R., and Defries, R., 'Classification trees: An alternative to traditional land cover classifiers', Int. J. of Remote Sensing, 17, , Lillesand, T. M., and Kiefer, R. W., Remote Sensing & Image Interpretation, 3rd ed., J. Wiley, New York, Ministry of Housing, Utilities and Urban Communities, General Organization for Physical Planning, Summary of the long-range urban development plan of the greater Cairo region, MOH, Cairo, Egypt, Ru-Ye, W., 'An Approach to tree-classifier design based on hierarchical clustering', Int. J. of Remote Sensing, 7, 75-88, Ru-Ye, W., 'An Approach to tree-classifier design based on splitting algorithm', Int. J. of Remote Sensing, 1, 89-14, 1986.