DefinePK

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 implementation. 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 the lesser computational complexity compared to the traditional feature extracting methods. Multikernel Sparse representation was also had 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 has 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 measures the classification accuracy, for images with higher and lower illumination and the images are given as input to analyze the robustness to direction change, performance with different number of classification classes, performance by changing the number of training and testing templates.