Multiple Kernel Based Remote Sensing Vegetation Classifier with Levy Optimized Subspace

Abstract

Vegetation classification in remote sensing (RS) applications details a rich source of information on land use/land cover decision making. To classify the mixed land-cover area of diverse categories through RS imagery, robust classification methods and their techniques act as a substratum for thematic interpretation. Though many classification techniques have been raised for the study of remote sensing images, support vector machine (SVM) has received huge attention. In order to handle non-linear separable high dimensional feature space, optimized subspace-based classifier acts as a bedrock for improved accuracy as accuracy is still a primary concern to design efficient classifiers for nonlinear separable feature space. In this work, a classifier Enhanced Entropy based Multiple Kernel Support Vector Machine with Levy optimized subspace has experimented for vegetation classification. Since SVMs are a typically linear methods, they can be easily derived into non-linear decision criteria by substituting the inner products with kernel functions. The results indicate that the proposed method is getting better accuracy than the existing Methods.