Semisupervised Feature Extraction With Neighborhood Constraints for Polarimetric SAR Classification

The supervised feature extraction methods have a relative high performance since the discriminating information of classes is introduced from large quantities of labeled samples. However, it is labor intensive to obtain labeled samples for terrain classification. In this paper, in order to reduce the cost of labeled samples, a novel semisupervised algorithm with neighborhood constraints (SNC) is proposed for polarimetric synthetic aperture radar (PolSAR) feature extraction and terrain classification. A number of PolSAR features of each pixel and its neighbors are used to construct a spatial group, which can represent the central pixel and weaken the influence of speckle noise. Then, with the class information from a few of pixels and the neighborhood constraints, an objective function is designed for the estimation of a nonlinear low-dimensional space. Finally, the spatial groups in the original high-dimensional space are projected to this low-dimensional space, and a low-dimensional feature set is obtained. The redundancy among the features is reduced. Additionally, unlike the conventional semisupervised algorithms, because the local spatial relation of PolSAR image is utilized, the extracted features not only are discriminating but also preserve the structure of the PolSAR data, which can enhance the classification accuracy. Experiments using the extracted features for classification are performed on both the synthesized PolSAR and real PolSAR data which are from the AIRSAR, RADARSAT-2, and EMISAR. Quantitative results indicate that SNC improves the separability of features and is superior to state-of-the-art feature extraction algorithms with a few labeled pixels.