CLASSIFICATION OF FULLY POLARIMETRIC SAR SATELLITE DATA USING GENETIC ALGORITHM AND NEURAL NETWORKS

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1 CLASSIFICATION OF FULLY POLARIMETRIC SAR SATELLITE DATA USING GENETIC ALGORITHM AND NEURAL NETWORKS Iman Entezari 1, Babak Mansouri 2, and Mahdi Motagh 1 1 Department of Geomatics Engineering, College of Engineering, University of Tehran, Tehran, Iran {entezari, motagh}@ut.ac.ir 2 International Institute of Earthquake Engineering and Seismology, Tehran, Iran #26 Arghavan St., N. Dibaji, Farmanieh, Tehran, Iran, Code: Tel: , Fax: mansouri@iiees.ac.ir ABSTRACT A novel method for supervised classification using fully polarimetric SAR data is proposed in this research. First a set of polarimetric parameters are extracted from SAR complex analysis and target decomposition theorems. Due to a large number of polarimetric parameters, the heuristic search method of Genetic Algorithm (GA) is used to select an optimum set of parameters as the feature vector. A multilayer feedforward neural network, which is a non-parametric classifier, is used to classify the features and to produce land cover maps. This procedure is implemented to fully polarimetric ALOS-PALSAR datasets of Tehran in order to assess the accuracy of the method. The final results show that the proposed algorithm provides high accuracy land cover identification/classification with an overall accuracy of 98.68% (Kappa coefficient of 97.86%). Keywords: SAR Polarimetry, Polarimetric Parameters, Genetic Algorithm, Neural Networks, Classification. INTRODUCTION Land cover identification and classification studies are important in delineating features associated with the built environment. The ability to differ between land cover types is necessary for urban planning and also in producing urban database. Fully polarimetric SAR image analysis shows unprecedented advantages in the identification and classification of different land types as compared with the single-channel SAR results. One of the most important aspects of using fully polarimetric data is the feasibility of selecting meaningful indices from numerous polarimetric parameters. These parameters can assist in the interpretation of the scene under study. Many studies have proposed different methods for classification of fully polarimetric SAR images [1-5]. These methods are usually based on employing polarimetric parameters in parametric and nonparametric classifiers. For example, parametric classifiers such as Maximum Likelihood and Minimum Distance have been used for classification purpose [1]. Also, non-parametric classifiers such as those based on Neural Networks and SVMs (Support Vector Machines) have been employed in supervised classification of fully polarimetric SAR data [2-4]. A novel method for supervised classification of the fully polarimetric SAR data is presented in this research. Different sets of polarimetric parameters are first extracted from SAR complex analysis and target decomposition theorems. From SAR complex analysis, parameters are calculated in both linear and circular polarization bases. Also, both coherent and incoherent decomposition theorems are considered for computing the parameters from target decomposition techniques. Secondly, due to a large number of polarimetric parameters, the feature selection method of Genetic Algorithm (GA) is used to select an optimum set of parameters as the feature vector. Finally, since the probability distribution function of some of the parameters is unidentified, a multilayer feedforward neural network which is a non-parametric classifier is used for classification stage. To assess the accuracy of the proposed method, fully polarimetric ALOS-PALSAR data of Tehran is processed. The results show that our classification algorithm provides a very high level of accuracy for land cover identification and classification. 1

2 METHODOLOGY Polarimetric SAR parameters computation Different sets of polarimetric parameters can be derived from SAR complex data analysis and polarimetric decomposition techniques. The scattering matrix can be computed in any orthogonal polarization basis considering SAR complex analysis. In this research, we consider the conventional linear and circular polarization bases. It should be mentioned that the scattering matrix in circular basis is computed using the change-of-basis theory [6]. The backscattering cross sections in linear and circular basis, ratios between the scattering matrix elements in linear and circular basis, ratios of the scattering matrix elements to the Span (sum of the intensities of four polarimetric channels) in linear and circular basis, and the polarimetric coherency in linear and circular basis and computed. Furthermore, many parameters can be obtained from different types of target decomposition techniques which provide information about scattering mechanisms in the scene. Here, the parameters including Pauli, Krogager, Freeman, Yamaguchi, H/A/ coefficients, and eigenvalues of the coherency matrix are also calculated from polarimetric decomposition techniques. Therefore, the extracted parameters in this research are listed as follows: Backscattering cross sections in linear and circular basis (6 features) Ratios between scattering matrix elements in linear basis and circular basis (6 features) Ratios of the scattering matrix elements to the Span in linear and circular basis (6 features) Polarimetric coherence in linear and circular basis (6 features) Pauli decomposition coefficients (3 features) Krogager decomposition coefficients (3 features) Freeman decomposition coefficients (3 features) Four component Yamaguchi decomposition coefficients (4 features) H/A/alpha parameters and combinations of H and A: HA, H(1-A), (1-H)A, and (1-H)(1-A) (the total of 7 features) Eigenvalues of the coherency matrix (3 features) Pedestal Height (1 features) Feature Selection The aim of feature selection is to find a set of features that yield the best classification result compromising computational efforts [7]. Because of three main reasons, feature selection is necessary for target identification and classification [7]. Firstly, the computational time is decreased by using fewer input features. Secondly, since some of the features may have considerable correlation, the complexity of the classification problem can be high and thus, employing many features does not necessarily increase the performance and the accuracy of the classification. Thirdly, in many cases the number of training data is limited. Therefore, in both parametric and standard non-parametric classifiers, the performance of the classification is decreased when the number of features in the input space is increased. In this research, the Genetic Algorithm is used for feature selection [8]. Fig. 1 shows the steps of implementing GA in optimization problems. The procedure of implementing Genetic Algorithm has three major steps as shown in Fig. 1. Firstly, the initial population which is a set of chromosomes should be created. Chromosomes are vectors of binary values. A vector value is associated with each classification feature. The length of the vector is the number of the extracted polarimetric SAR parameters. Secondly, the fitness function, which evaluates the ability of each chromosome to be selected as a parent, must be computed. The fitness function, in this research, is the Overall Accuracy of classification computed by the well-known Minimum Distance Classifier (MDC). The MDC is used mainly because this classifier can linearly separate different classes. Therefore, it is expected that the selected features yield the faster convergence of the neural network in the learning phase. Finally, the reproduction phase must be completed. In this phase, the process of parent selection is performed and then using the genetic operators such as crossover and mutation, the next generation is produced. The Tournament method is selected as the parent selection technique. According to the diagram, the procedure of reproduction and fitness computation are repeated till the stop criterion is met. 2

3 Table 1. Main specifications of the ALOS-PALSAR data Image Mode Full Polarimetric Center Frequency 1270 MHz Wavelength 23.6 cm (L-band) Band width 14 MHz Transmission Power 2 kw (peak power) Look Direction Right Look Angle 21.5 degree Swath Width 30 Km Range Resolution 30 m Azimuth Resolution 5 m Figure 1. Steps in implementing the Genetic Algorithm Neural Network Classifier Artificial Neural Networks are widely used in remote sensing applications such as pattern recognition and classification. Neural Networks have two important advantages in classification problems [9]: 1) no necessity to have a prior knowledge about the statistical distribution of the input data, and 2) capability to implement nonlinear discrimination functions. The multilayer feed-forward neural network is used in this research. The input layer of the network has a number of neurons that are equivalent to the number of input features. The number of neurons in the output layer is corresponding to the number of classes which have to be categorized. Also, a decision should be made on the number of hidden layers and their neurons. Kolmogorov stated that any discriminant function can be derived by a three-layer feedforward neural network [10]. Based on this rule, in this research, one hidden layer is considered for the neural network to identify the classes. Furthermore, a convenient rule of thumb is used to determine the number of neurons in the hidden layer of a threelayer feedforward neural network [9]. This rule confirms that the number of neurons in the hidden layer of a three-layer feedforward neural network (N H ) with N I input neurons and N O output neurons should be an integer close to: (1) After determining the structure of the network, the training phase must be completed using the selected training data for each class. The network is trained by Back-Propagation algorithm. The strategy in back-propagation learning algorithm is to update the weights in each layer in a way that minimize the total error. RESULTS AND DISCUSSION Study Area and Polarimetric Data The ALOS-PALSAR data (SLC - single look complex) as acquired on April 2009, over Tehran, is used. The specification of the used data is listed in Tab. 1. To make the ground resolution in both range and azimuth directions identical, a multi-looking with a 6x1 window size (i.e. 6 pixels in azimuth direction and 1 pixel in range direction) is applied on the original data. Also, for computing the polarimetric parameters, a 3x3 window size is adopted. Implementation The methodology of supervised classification of fully polarimetric SAR images is according to the flowchart shown in Fig 2. The objective is to identify different types of spectral classes to produce land cover map with four main classes of interest, namely built environments, forest areas, 3

4 agricultural regions, and bare fields. The training and test data are selected by visual interpretation using a co-registered optical VHR image of the scene. The location of the training data, the test samples and the optical image of the scene are shown in Figs. 3, 4, and 5 respectively. Also, the number of training and test samples is listed in Tab. 2. Figure 2. Flowchart of the proposed supervised classification using polarimetric images Figure 3. Pauli image - Training samples Figure 4. Pauli image - Test samples Table 2. Number of training and test samples Class type Training samples Test samples Built environments Forest areas Agricultural areas Bare fields Figure 5. VHR image of Tehran (Google Earth) The Genetic Algorithm was applied to select the optimum feature set. Selecting 7 up to 10 features was considered as a constraint for GA. This constraint was because of the fact that due to the limitation of the training samples size, selecting more than 10 features can cause a complexity in classification problem. Also, the stop criterion of GA process was considered the identicality of the 70% of population. Finally, GA stopped after 11 iteration and nine polarimetric parameters were selected as the feature vector for classification. They are namely: ratio between HH and VV channels, ratio between RR and LL channels, ratio of RR and Span, polarimetric coherence between HH and HV, polarimetric coherence between HV and VV, diplane and helix components of the Krogager decomposition, combination term of HA, and the pedestal height. Thus, these nine parameters are employed in the neural network classifier as the input feature vector. 4

5 Since the Genetic Algorithm selected nine parameters as the input feature set, the number of neurons in the input layer is nine. Also, four spectral classes should be classified. Therefore, based on the formula introduced in Eq. 1, the number of neurons in the hidden layer is six. The structure of the neural network is (See Fig. 6). Besides, the values of the parameters needed in the training phase were set by trial and error. Figure 6. Structure of the Neural Network Classification Results In order to evaluate the accuracy and the performance of the proposed method, three steps must be performed. Firstly, the training samples are presented to the neural network repeatedly until the network learns them. Secondly, the test samples are classified and finally, the confusion matrix is formed and the statistical indicators are computed to assess the performance of the classification. Tab. 3 presents the confusion matrix derived from the proposed method. Reference Data Table 3. Confusion matrix derived from the proposed method Classified Data Land Cover Built Env. Forest Area Agricultural Regions Bare Fields Total Built Env Forest Area Agricultural Regions Bare Fields Total Σ=3256 In this research, we computed the statistical indicators including: Overall Accuracy (OA), Kappa Coefficient (K), Producer s Accuracy (PA), and User s Accuracy (UA). This method has reached the Overall Accuracy of 98.68% and Kappa value of 97.86%. These values represent the high efficiency of the proposed method in supervised classification of fully polarimetric SAR data. Also, the values of PA and UA for different types of land covers and the final classified map produced by the proposed method are shown in Figs. 7 and 8 respectively. As can be seen in Fig. 7, the proposed method has identified all classes with a very high level of accuracy. Specially, the values of PA and UA for built environments are considerable. The method has been completely successful in identification of built environments. Identification and classification of built environments has always been an important field of study because it represents the building inventory as a major urban element-at-risk in man-made and natural disasters. Delineating the buildings from the entire urban scene is the basic step in applying change detection algorithms for revealing physical damages. Finally, it seems the high performance of the proposed classification method arises from the fact that the four considered classes are extremely different, spectrally. Also, it should be mentioned that since the proposed methodology was just implemented using a single dataset, the results have not absolute validity but may indicate a general trend for similar data and classes of interest. CONCLUSIONS A novel method for supervised classification of fully polarimetric SAR images was proposed. The proposed methodology is based on the Genetic Algorithm feature section and multilayer neural network classifier. The methodology was implemented on the fully polarimetric ALOS-PALSAR data as acquired in April 2009, over Tehran, Iran. The results of classification show that a high degree of accuracy for land cover classification and land cover mapping can be obtained. Also, it is worth 5

6 mentioning that this methodology should be implemented on the different polarimetric datasets with more spectral classes to asses the reliability of the method accordingly. Figure 7. User s and Producer s accuracies Figure 8. Classification map ACKNOWLEDGMENT The authors would like to thank the Japan Aerospace Exploration Agency (JAXA) for acquiring fully polarimetric ALOS-PALSAR data over Tehran and the European Space Agency for providing the data using the ALOS proposal No. AOALO3740. REFERENCES [1] Alberga, V. (2005) Comparison of Polarimetric Methods in Image Classification and SAR Interferometry Applications. Ph.D. dissertation, Tech. Univ. Chemnitz, Chemnitz, Germany. [2] Shah Hosseini, R., Entezari, I., Homayouni, S., and Mansouri, B. (2009) Classification of Polarimetric SAR Images Using Support Vector Machines. In Proceedings of the 30th Canadian Symposium on Remote Sensing, Lethbridge, Alberta, Canada. [3] Fukuda, S. and Hirosawa, H. (2001) Polarimetric SAR image classification using support vector machines. IEICE Trans. Electronics, vol.e84-c, no.12, pp [4] Song, C.J., Yun, S., and Hui, L. (2004) Classification of Polarimetric SAR imagerary based on Target Decomposition and neural network classifier. In Proc. of ACRS 04, November, [5] Lee, J.S., Grues, M.R., and Kwok, R. (1994) Classification of Multi-look Polarimetric SAR Imagery Based on Complex Wishart Distribution. International Journal of Remote Sensing, vol. 15, pp [6] Lüneburg, E. (1996) Radar polarimetry: A revision of basic concepts, in Direct and Inverse Electromagnetic Scattering. (H. Serbest and S. Cloude, Eds.), Pittman Research Notes in Mathematics Series 361, Addison Wesley Longman, Harlow, U.K. [7] Bhanu, B. and Lin, Y. (2003) Genetic Algorithm Based Feature Selection for Target Detection in SAR Images. Image and Vision Computing, vol. 21(7), pp [8] Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. [9] Duda, R.O., Hart, P.E., and Stork, D.G. (2001) Pattern Classification (2nd edition). John Wiley and Sons, New York. [10] Miller, D.M., Kaminsky, E.J., and Rana, S. (1995) Neural Network Classification of Remote Sensing Data. Computers and Geosciences, vol. 21, pp