1. AN.Valliyappan 2. Assistant Professor/ECE VCET, Madurai VCET, Madurai

Transcription

1 Gabor Features Based Classification of Multispectral Images Using Clustering with SVM 1 1 Assistant Professor/ECE VCET, Madurai ushadears@gmail.com AN.Valliyappan 2 2 U.G Student/ECE VCET, Madurai valliece1@gmail.com Abstract Classification of multispectral remotely sensed data is investigated at present with a special focus on uncertainty analysis in the produced land-cover maps. A richer set of land-cover classes are observable in satellite imagery than ever before due to the increased sub-meter resolution. The objective of this proposed work is to present an efficient technique for classification of multispectral satellite images into different land cover classes. In the proposed classification technique, initially pre-processing is done where the input image is subjected to a set of preprocessing steps which includes Gaussian filtering and RGB to Lab color space image conversion. Subsequently, the texture features of the filtered image are extracted by applying Gabor filters. Then, the obtained feature vector is subjected to fuzzy c-means clustering for segmentation of the image followed up by training of the SVM, and finally the classification step, where the cluster centroids are subjected to the trained SVM to obtain the different land cover classes. The experimentation is carried out using the multi-spectral satellite images and the analysis ensures that the performance of the proposed technique is improved and accuracy is higher in compared with traditional methods. Index Terms Classification, Clustering, Fuzzy c-means, Gabor filter, Support vector machine (SVM) 1 INTRODUCTION REMOTE sensing images are an important source of information regarding the Earth s surface. These images provide quantitative and qualitative information that reduces complexity of field work and study time. For many applications, the underlying land-cover information from such images is needed. Supervised or unsupervised classification techniques exist in the literature to generate a reliable land-cover map of the geographical area captured in the image [1].There is a strong need of effective and efficient mechanisms to extract and interpret valuable information from massive satellite images. Satellite image classification is a powerful technique to extract information from huge number of satellite images and is also a process of grouping pixels into meaningful classes. It is a multi-step workflow. Usually, a classification system makes a classification map of the meaningful features or classes of land cover sections in a part. Regardless of all the advantages, classification of land-cover using multispectral imagery is a difficult subject because of the complexity of landscapes and the spatial and spectral resolution of the images being engaged. An efficient method capable of arranging the spectral and spatial information existing in the multispectral data can increase the accuracy level of the classification in a good way when matched with the traditional non-contextual information based techniques. Multispectral image classification is considered to be a combined project of 10 both image processing and classification methods. Usually, image classification [8], in the process of remote sensing is the method of referring pixels or the basic units of an image to the classes. It is most likely to create groups of similar pixels found in image data into classes that match the informational categories of user interest by matching the pixels to one another and to those of the said identity. Many techniques for image classification have been introduced and numerous areas like image analysis and pattern recognition use the vital term, classification. In many circumstances, the classification itself may become the entity of the analysis and serve as the ultimate matter. Due to this, image classification has grown and established as a major tool for learning digital images. Furthermore, the choice of the ideal classification method to be used can have a considerable effect on the outcomes of it. The available literature has a number of supervised techniques to overcome the multispectral data classification problematic scene. The statistical technique used in [2], maximum likelihood classifier was shown to be less accurate for land-cover classification because of its computational complexity. The clustering results obtained from graph cut initialization [3] and EM algorithm was accurate enough to provide varied results, which weren t stabilised to meet classification standards. The cluster ensemble strategy adopted in [4] for unsupervised classification seems to be of less use

2 because of the varying training data sets given by the 3 PRE-PROCESSING user. The main focus of this paper is to provide an efficient feature extraction and classification strategy to classify land cover classes with greater accuracy and performance with chosen classifier. This Paper is organized as follows. Section 2 details the proposed classification technique. Pre-processing steps are mentioned in Section 3. Section 4 depicts the Gabor texture feature extraction. Section 5 illustrates the fuzzy c-means clustering. Section 6 provides the classification using SVM. Experimental results are shown in Section 7. Section 8 concludes this paper with discussion. 2 PROPOSED METHODOLOGY This section presents the proposed technique of classification of multispectral satellite image using clustering with SVM classifier. Initially in our proposed classification technique, pre-processing is done where the input image is subjected to a set of pre-processing steps which includes Gaussian filtering and RGB to Lab colour space image conversion so that the image gets transformed suitably for feature extraction followed up by segmentation. The Gabor texture features are extracted by application of Gabor filters for different scales and orientations. The extracted features are segmented using the fuzzy clustering algorithm. Training data selection is carried out for SVM and finally, classification of the multispectral satellite image using SVM is done based on the trained data and the centroid pixel values. The block diagram of the proposed method is given in the figure 1. Multispectral Image Multispectral images cannot be fed directly into the SVM for training and testing. The input multispectral satellite image is subjected to a set of pre-processing steps so that the image gets transformed suitably for further processing. Two step pre-processing procedure is applied in which first the input image is passed through a Gaussian filter to reduce the noise and get a better image fit for segmentation. Passing the image through the Gaussian filter also enhances the image quality. In the second step of pre-processing, we convert the image from the RGB model to Lab colour space Image which makes it more fit for texture feature extraction and for the segmentation process by the use of clustering technique. 3.1 Gaussian Filter: A Gaussian filter is a filter whose impulse response is a Gaussian function. Gaussian filters are developed to avoid overshoot of step function input while reducing the rise and fall time. This character is very much linked to the fact that the Gaussian filter has the minimum possible group delay. In mathematical terms, a Gaussian filter changes the input signal by convolution with a Gaussian function; this change is also called the Weierstrass transform. The Gaussian function is nonzero for x [-, ] and would supposedly need an infinite window length. The filter function is supposed to be the kernel of an integral transform. The Gaussian kernel is continuous and is not discrete. The cut-off frequency of the filter can be taken as the ratio between the sample rate Fs and the standard deviation σ. Gaussian filtering Conversion of RGB to Labcolourspace image Gabor Texture feature extraction Segmentation using clustering Training data selection for SVM Classification using SVM Classified output Figure 1. Block diagram of the proposed method 11 f c = f s σ The 1D Gaussian filters is given by the equation g x = 1 2 2πσ e x 2σ 2 The impulse response of the 1D Gaussian Filter is g x = 1 e σ2u2 2 2πσ 3.2 Conversion of RGB to Lab colour space Image: A Lab colour space is a colour-opponent space with dimension L for lightness and a and b for the colouropponent dimensions, based on nonlinearly compressed CIE XYZ colour space coordinates. Different from the RGB and CMYK colour models, Lab colour is developed to approximate the human vision. It aims for perceptual uniformity, and its L component relatively corresponds to human perception of lightness. It is

3 therefore used to make accurate colour balance elementary function. Gabor features are constructed corrections by changing the output curves in the a and from responses of Gabor filters in (1) or (2) by using b components, or to regulate the lightness contrast multiple filters on several frequencies fm and using the L component. In RGB or CMYK spaces, orientations θn. Frequency in this case corresponds to which model the output of physical devices instead of scale information and is thus drawn from the human visual perception, these changes are done corresponding blend modes in the editing application. sf m = k m f max, m = 0,1,, M GABOR TEXTURE FEATURE EXTRACTION In image processing applications, Gabor filter is the successful technique for construction of image features and Gabor features selection [5]. Features based on Gabor filter responses perform remarkably well in many modern problems and applications of computer vision and image processing. Texture analysis has a long history and texture analysis algorithms range from using random field models to multi resolution filtering techniques such as the wavelet transform. In this paper, the Gabor filter based feature extraction is performed and after feature extraction, feature selection is applied for the training of these features with the help of well known classification technique called support vector machine (SVM). The use of Gabor filters in extracting textured image features is motivated by various factors. The Gabor representation has been shown to be optimal in the sense of minimizing the joint two-dimensional uncertainty in space and frequency. Gabor features have been used in several images. These filters can be considered as orientation and scale tuneable edge and line (bar) detectors, and the statistics of these micro features in a given region are often used to characterize the underlying texture, colour and shape information. The core of Gabor filter based feature extraction is the 2D Gabor filter function: φ x, y = x = x cos θ + y sin θ f 2 f 2 πγμ e ( y 2x 2 + f2 y 2y 2 )ej 2πfx y = - x sin θ + y cos θ (1) This function has the following analytical form in the frequency domain: π 2 φ u, v = ef 2 (y 2 u f 2 + n 2 v 2 ) u = u cos θ + v sin θ v = - u sin θ + v cos θ (2) In the frequency domain (2) the function is a single realvalued Gaussian centred at f. The Gabor filter in (1) and (2) is a simplified version of the general 2D form devised by Daugman from the Gabor s original 1D 12 Wheref m is the m th frequency, f 0 = f max is the highest frequency desired, and k > 1 is the frequency scaling factor. The filter orientations are drawn from θ n = n2π N, n = 0,1,, N 1 (4) Where θ n the n th orientation and N is is the total number of orientations. Gabor texture features are extracted by applying a bank of scale and orientation selective Gabor filters to an image. Raw features are the complex-valued responses of a set of multi-resolution Gabor filters. A single global Gabor texture feature is formed by computing the mean and standard deviation of the filtered images. A 2 dimensional feature vector, Gabor GLOBAL, is formed as Gabor GLOBAL = [μ11, σ11, μ12, σ12,.., μrs, σrs] Where μrs and σrs are the mean and standard deviation of frs (x, y). Finally, to normalize for differences in range, each of the 2RS components is scaled to have a mean of zero and a standard deviation of one across a dataset. Using a classifier, the features can be effectively classified in order to detect and identify real world structures in images. 5 FUZZY C-MEANS CLUSTERING Fuzzy c-means (FCM) is a technique of clustering [6] which permits one piece of data to two or more clusters. This technique was introduced by Dunn in 1973 and renewed by Bezdek in 1981 and it is mostly employed in pattern recognition. It is based on minimization of the following objective function: J m = N C i=1 j =1 μ ij m x i c j 2, 1 m Where m is any real number greater than 1, μ ij is the degree of membership of x i in the cluster j, x i is the i th of d-dimensional measured data, c j is the d-dimension center of the cluster, and * is any norm expressing the similarity between any measured data and the center. Fuzzy partitioning is carried out through an iterative optimization of the objective function shown above, with the update of membership μ ij and the cluster centers c j by:

4 μ ij = c j = INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY Final Classification using SVM C ( x i c i x i c ) 2 m 1 k =1 k N i=1 N m i=1 μ ij μ ij m x i This iteration will stop when his iteration will stop when max ij { μ k=1 ij μ k ij < ε}, where ε is a termination criterion between 0 and 1, whereas k is the iteration step. This procedure converges to a local minimum or a saddle point of J m. The obtained Gabor feature vector from the previous step is subjected to Fuzzy c-means clustering and different cluster centroids initializations are obtained. 6 SUPPORT VECTOR MACHINE Support Vector Machines (SVM) is a statistical learning based classification system. The SVM sections the classes with respect to a decision surface that maximizes the margin between the classes. The surface is normally known as the optimal hyper plane and the data points closest to the optimal hyper plane are known as the support vectors. An SVM [7] classifies data by finding the best hyper plane that separates all data points of one class from those of the other class. The best hyper plane for an SVM means the one with the largest margin between the two classes. Margin means the maximal width of the slab parallel to the hyper plane that has no interior data points. The support vectors are the most important elements of the training set. These support vectors are the data points that are closest to the separating hyper plane; these points are on the boundary of the slab. For multiclass classification, the pair wise classification strategy is regularly made use of. The result of the SVM classification is the decision values of each pixel for each of the class. This is employed for probability estimates. 6.1 Training Data selection for SVM In this section, the training data selection given to the SVM is discussed for the classification purpose. The proposed technique aims to classify the image into land cover classes. This is effectively done making use of the colour features in the satellite image. Each of the elements in earth has a colour by which it is distinguished. Hence in-order to classify the image using the SVM, we make use of the colour of these earthly elements. The colours for respective land covers have been identified and these colour details are given to the SVM classifier for classification purpose along with cluster centroids obtained from the feature set. Some of the elements that come under the land cover include that of vegetation, soil, mud, crops. These colour details are given to the SVM and based on this data classification is done in the final step. 13 The pre-processed multispectral satellite image subjected to fuzzy incorporated clustering gives rise to clusters. Here it can be seen that each member in a cluster will have almost similar pixel values and differ from the centroid value of the cluster by only a small amount. Hence the centroid value can represent all the pixels in the clusters. Hence, by performing single step of classifying the centroid of a cluster will act like as multiple steps of classifying all the pixels in the cluster. This result in reducing the number of the inputs to the classifier system which reduce the classifier complexity and also the time incurred. Suppose we consider the i t cluster having n elements where each pixel having a value of P k.then, the centroid value of the i t cluster, O i can be calculated as follows O i = n k=1 P k n Similarly, repeat the process for all the clusters to obtain the centroids values for each of the clusters. Suppose there are N numbers of cluster, then centroid set O = {O 1, O 2, O 3,..., O N }, will be given as the input to the SVM classifier. These centroids set are classified based on the trained data given to the SVM before and the classified result as land covers is obtained. 7 EXPERIMENTAL RESULTS AND DISCUSSION In this section, the result of the above proposed classification technique has been presented. Figure 3 shows the input multispectral image taken for experimentation. Figure 4 provides the Gaussian filtered image. The magnitude and real parts of applied Gabor filters are depicted in Figure 5 & 6. Segmented image with different cluster centroid initializations is shown in Figure 7. Figure 8 shows the plot of trained and classified SVM. The hyper plane classifying the input image into different classes has been shown in figure 8.. Points nearer to hyper plane represented by circular dots are Support vectors. Figure 9 shows the final classified output with different land cover classes. The proposed methodology is applied to different satellite sensors images, whose output is shown in figure 10 and 11. The proposed methodology is compared with the traditional method. The performance evaluation is given in Table 1 which shows that the proposed methodology gives better accuracy than the traditional methods.

5 Figure 6: Real parts of Gabor filters Figure 3: Input Multispectral Image The multispectral image taken as input for experimentation is the IKONOS image, which is further subjected to pre-processing steps and then featre extracted Figure 7: Fuzzy c-means clustered image Figure 8: Plot of Trained and Classified SVM Figure 4: Gaussian Filtered Image Figure 5: Magnitude of Gabor filters Figure 9: Classified Image Figure 10: Quick Bird Classified Image 14

6 10 REFERENCES [1] Sunitha Abburu, Suresh Babu Golla Satellite Image Classification Methods and Techniques: A Review, International Journal Of Computer Applications ( ) Vol.119, No.8, Jun [2] L.Bruzzone and D.F.Prieto, Unsupervised retraining of a Maximum likelihood classifier for the analysis of Multispectral Remote Sensing images, IEEE Trans.Geoscience, Vol.39, No 2, Feb-2001 Figure 11: Rapid Eye Classified Image Wavelet Decomposition + FCM +SVM Proposed Methodology (Gabor Features + FCM +SVM) Table 1: Performance Evaluation Techniques Image Dataset Accuracy Ikonos 76.50% Quickbird 75.66% Rapid eye 78.23% 8 CONCLUSION Ikonos 91.09% Quickbird 86% Rapid eye 85.55% In this paper, an efficient image classification technique has been presented for multispectral remote sensed satellite images with the aid of clustering and Support Vector Machines (SVM). The proposed classification technique comprises of five phases namely preprocessing, Gabor feature extraction, segmentation, training of SVM and final classification using SVM. After pre-processing, the input image is converted into an image fit for segmentation. Cluster centroids are obtained after application to Fuzzy clustering algorithm. SVM is trained according to the data given. Finally the image is given as an input to the trained SVM, which classifies the multispectral satellite image into land cover regions according to the trained data and pixel values. The experimental results have demonstrated the effectiveness of the proposed classification technique. The experimentation is carried out for different data sets and the analysis ensures that the performance of the proposed technique is better than the traditional feature extraction and classification algorithms. [3] M.Tyagi, F.Bovolo, A.K.Mehra, A context sensitive clustering technique based on graph cut initialization and expectationmaximization algorithm, IEEE Geoscience Remote Sens.Lett., Vol.5, No.1, Jan-2008 [4] N.Alajlan, N.Ammour, Y.Bazi, H.Hichri, A cluster ensemble method for robust unsupervised classification of VHR remote sensing images, in proc. IEEE IGARSS, 2011 [5] Manali Jain, Amit Sinha, Classification of satellite images through gabor filter using SVM, International Journal of Computer ApplicationsVol.116, No.7, Apr-2015 [6] Biplab Banerjee, Francesa Bovolo, L.Bruzzone, A New Self- Training based unsupervised satellite image Classification technique using Cluster Ensemble Strategy,IEEE Geo Science and remote sensing letters, Vol.12, No.4, Apr 2015 [7] Manali Jain, Amit Sinha, Classification of multispectral Satellite Images using clustering with SVM Classifier, International Journal of Computer Applications,Vol.35, No.5, Dec-2011 [8] K.Bahirat,F.Bovolo (2012), A Novel domain adaptation Bayesian classifier for updating land cover maps with class differences in source and target domains, IEEE Geoscience Remote Sens.Lett., Vol.50, No.7, pp AUTHOR PROFILE Mrs.S.Gandhimathi@usha is an Assistant Professor in the department of Electronics & Communication Engineering at Velammal College of engineering and technology, Madurai, Tamilnadu, India. She is having thirteen years of experience in teaching. Her current area of research is Satellite Image Processing. AN.Valliyappan is a Final year UG student in the department of Electronics & Communication Engineering at Velammal College of engineering and technology, Madurai, Tamilnadu, India. His current area of research is Satellite Image Processing. 9 ACKNOWLEDGEMENT We thank the management of Velammal College of engineering and technology for providing us the opportunity to carry out this project in a successful manner. 15