Optimal Combination of Polarimetric Features for Vegetation Classification in PolSAR Image

Polarimetric features of PolSAR images include inherent scattering mechanisms of terrain types, which are important for classification and other Earth observation applications. By using target decomposition methods, many polarimetric scattering components can be obtained. Besides, the elements of a coherency/covariance matrix, as well as polarimetric descriptors, such as SPAN, single-bounce eigenvalue relative difference/double-bounce eigenvalue relative difference, etc., can also provide characteristic information. In fact, more and more polarimetric decomposition components and descriptors have been proposed; the computation cost increases if all of them are employed as the input of the classification process. Although all these features obtained from the coherency/covariance matrix are not independent, still, finding out which ones are significant for the classification of different terrain types will improve the understanding of scattering mechanisms. In this article, the effective polarimetric feature combination is studied based on the vegetation classification performance of support vector machine (SVM) and nearest-regularized subspace (NRS) machine learning approaches, as well as their combinations with a Markov random field (MRF). A framework on the basis of similarity and the orthogonal subspace projection (OSP) method in a hyperspectral area is used to select the polarimetric features. For the airborne PolSAR data in Flevoland, The Netherlands, 107 polarimetric features are extracted, including matrix elements, target decomposition components, and polarimetric descriptors. A subset is selected by using the proposed and OSP methods. They have a good classification accuracy evaluated by SVM+MRF and NRS+MRF classifiers. However, when the SVM and the NRS are used without combining spatial information of the MRF, the features selected by the proposed framework with correlation coefficient criteria have much better classification performance than those of OSP and principal component analysis.