Agricultural crop-type classification of multi-polarization SAR images using a hybrid entropy decomposition and support vector machine technique

Abstract

This article presents the application of a hybrid classification technique of entropy decomposition and support vector machine (EDSVM) for crop-type categorization. It takes the advantage of the desired parameters from the entropy decomposition (ED) method and the statistical learning method based on the support vector machine (SVM) method that determines the optimal separation between classes in a higher dimensional feature space to improve on the existing classification results. ED is capable of extracting valuable decomposed parameters of entropy H and alpha α for image interpretation with analysis of the underlying scattering mechanisms. H demonstrates the randomness of the underlying scattering mechanisms and α is used to define the type of scattering mechanisms. However, in the application of agricultural crops where the scattering mechanisms of the crops are quite similar to each other, the distribution of the H and α in the H–α feature space overlaps from one class to another. Moreover, the drawback of ED is the arbitrariness of the boundaries for each class. To overcome this issue, SVM classifier is deployed to determine the decision boundaries by projecting the training sets of the classes into higher dimensional feature space. Hence, the hybrid EDSVM is developed to provide an alternative solution to improve the classification accuracy. In this article, EDSVM classifier is applied on a multi-crop field Airborne Synthetic Aperture Radar (AIRSAR) image of Flevoland in the Netherlands and the robustness of the classifier is evaluated. The classification is done with the purpose of separating the different types of crops with the characteristics of the scattering mechanism. At the same time, a hybrid entropy decomposition and neural network (EDNN) classifier method is developed to validate the effectiveness of the EDSVM classifier. As a result, EDSVM is proved to be robust and to yield a superior result compared with neural network (NN), SVM and EDNN classifiers.