Terrain classification based on spatial multi-attribute graph using Polarimetric SAR data

Recently the graph-based semi-supervised classification arouses vast amount of interest in remote sensing as it can utilize only a few labeled samples and large numbers of unlabeled samples to enhance the classification accuracy on various types of terrains. However in most of the conventional methods, multiple features (e.g. the scattering components, texture, color, etc) are concatenate together into a long vector for graph construction and classification. This not only ignores the physical attribute of features, but also causes so-called curse of dimensionality and limits the performances of classification. Inspired by the multi-view machine learning, we propose a spatial multi-attribute graph model and rank the attributes of polarimetric synthetic aperture radar (PolSAR) data for terrain classification in this paper. It firstly constructs multiple graphs according to the physical attributes of the groups of features based on different similarity metrics, then automatically optimizes a balanced weight for each graph and combines the spatial information between pixels for label propagation and classification. Since it takes into account of the physical properties from PolSAR data for feature fusion and graph construction, the mechanically feature stacking and curse of dimensionality can be avoided. Experimental results on synthesized PolSAR data and real ones show enhanced classification accuracy of the proposed method compared with state-of-the-art graph-based methods when only a small number of labeled samples are available. Our empirical studies also indicate that the covariance matrix play predominant roles for PolSAR classification.