VOL. 11, NO. 15, AUGUST 2016 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 9177 A NOVEL TEMPLATE MATCHING IMPLEMENTATION OF OBJECT BASED IMAGE CLASSIFICATION BASED ON MULTIKERNEL FUSION SPARSE REPRESENTATION Shivakumar G. S. 1 , S. Natarajan 2 and K. Srikanta Murthy 3 1 Department of Computer Science and Engineering, Srinivas Institute of Technology, Mangalore, India 2 Department of Information Science, PESIT, Bangalore, India 3 Department of Computer Science and Engineering, PESSE, Bangalore, India E-Mail: shivakumar_gs1@yahoo.co.in ABSTRACT This paper introduces and implements a novel object based image classification method on remote sensing images. The novelty introduced in this implementation is the application of a Multikernel Sparse Representation method on the object based image classification. The template-matching algorithm inspired from the object tracking implementation replaces the process of segmentation usually applied in object based image classification. The Multikernel fusion sparse representation based learning and prediction method is developed for remote sensing image classification. A particle filter framework for the sample template selection with the Multikernel Fusion Sparse Representation optimization technique is used to develop the image classification algorithm. The particle filter will act as the template-matching framework for our classification algorithm and the optimization of the observation model of this framework is carried out using the Multikernel Fusion Sparse Representation. Multikernel implementation has been proved to be more accurate than the feature extraction techniques since it extracts the internal intricacies of the image vector. The Kernels consume lesser memory space and lesser computational complexity compared to the traditional feature extracting methods. Multikernel Sparse representation has been proved to be more accurate and less computationally complex while implemented in other applications like the video object tracking. Affine transform based templates are extracted from the image which have to be trained and the kernel matrix is generated which is used for comparison with the templates extracted from the test images. Kernel Coordinate Descent (KCD) algorithm is used to find the similarity measure between the database kernel and the testing kernel. The weight values updated using the observation likelihood method that would indicate whether the test template matches with the database templates. The comparison is carried out with the multikernel method using the SVM classifier. The results that are observed are kappa coefficient and overall accuracy, which measure the classification accuracy, for images with higher and lower illumination and also the images are analysed for robustness to direction change and the classification performance for two different hyperspectral images. Keywords: active learning, sparse representation, remote sensing, classification. INTRODUCTION Conditional Probability is the probability of occurrence of an event provided another event had occurred. Posterior Probability is the conditional probability, which considers the relevance between the events that occurred due to the conditional probability. Particle framework defined in the literature [18] is based on the posterior probability method on the video tracking implementation. Posterior probability defines the most certain occurrence of an event. The recent literature used the particle filter framework on image classification where the filtering, labeling and statistics are carried out using particle filter framework [17]. Sparse representation is castoff to symbolize the data under study in a comparable and expressive manner in a smaller memory space. SR- based algorithm has a drawback of high computation cost and less accuracy which is overcome by using kernel sparse representation (KSR)-based algorithm using single feature kernel and it also avoids introducing large number of trivial templates which will speed up compared to SR- based methods. Furthermore to overcome the weakness of single feature in object description, multikernel fusion method is proposed for multiple features integration. Furthermore to improve the method to have higher classification accuracy we utilize a prediction strategy called the adaptive multikernel fusion. Sparse representation would be a signal specific basis pursuit method that would be a more accurate representation for any non-linear signal. The deeper insight of sparse representation into the data structure would make it eligible for the classification algorithms of remote sensing. The sparse representation is the most compact representation by using the linear combination of the building blocks of the data. Remote sensing image classification using sparse implementation and active learning method is taken into account for implementation. The active learning method is the process where the data under learning has to be decided by the human intervention in order to have higher redundancy [1]. SVMs can be considered as techniques which use hypothesis space of linear separators in a high dimensional feature space, trained with a learning algorithm from optimization theory that makes a learning bias derived from statistical learning theory [2, 3]. Support vector machine is one of the important methods for use in the active learning method. The classification was binary but now the multiclass Support Vector Machine has been introduced for better classification.