Semi-Supervised PolSAR Image Classification Based on Improved Tri-Training With a Minimum Spanning Tree

Abstract

In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods.