Unsupervised classification of forest from polarimetric interferometric SAR data using fuzzy clustering

Abstract

Fuzzy clustering algorithms have been successfully applied to POLSAR classification, but not to POLInSAR. In this paper, a Fuzzy C Means (FCM) clustering algorithm integrating the complementary physical information and statistical property contained in both polarimetric and interferometric data, is used for POLInSAR classification. At first, the area dominated by volume scattering is extracted from polarimetric information using unsupervised H-A-Alpha k-means Wishart classifier with the physical scattering mechanisms of different terrain types; and the volume scattering area (forest area) is further segmented in the feature space of the relative optimal interferomet-ric coherence spectrum A1 and A2. Then a robust unsupervised fuzzy C means (FCM) classifier initialized with the results of the segmentation is applied to the polarimetric interferometric coherency data sets corresponding to the volume scattering area. This will not only take into account the scattering mechanisms of the data, so that the results of the classification have definite physical meaning, but also avoid the problem that the initial value of FCM algorithm is difficult to identify. The proposed method is evaluated and compared with k-means Wishart classifier using repeat pass E-SAR L band polarimetric interfer-ometric SAR data and the corresponding auxiliary image. Preliminary results show that the proposed method has better performance.