Abstract
Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information. Moreover, the similarity measurement is a crucial factor for constructing a weighted graph. Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (PolSAR) image classification. For obtaining a satisfactory PolSAR image classification performance, a co-regularized graph embedding (CRGE) method by combing the two distances is proposed for PolSAR image feature extraction in this paper. Firstly, two weighted graphs are constructed based on the two distances to represent the data’s local structure information. Specifically, the neighbouring samples are sought in a local patch to decrease computation cost and use spatial information. Next the DR model is constructed based on the two weighted graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two weighted graphs. We employ two types of co-regularization and the corresponding algorithms are proposed. Ultimately, the obtained low-dimensional features are used for PolSAR image classification. Experiments are implemented on three PolSAR datasets and results show that the co-regularized graph embedding can enhance the performance of PolSAR image classification.
Highlights
Regardless of the influence of the light and the weather, polarimetric synthetic aperture radar (PolSAR) has the ability to gain high-resolution images
Wishart distance-based Laplacian embedding (WDLE) performs fairly worse than Wishart classifier (WC), principal component analysis (PCA) and polarimetric feature-based Laplacian embedding (PFLE)
The overall accuracy (OA) of the proposed co-regularized graph embedding (CRGE) is higher than other methods
Summary
Regardless of the influence of the light and the weather, polarimetric synthetic aperture radar (PolSAR) has the ability to gain high-resolution images. The Wishart classifier becomes one of most classical PolSAR image classification methods and is widely used. Afterwards, Lee et al [5] cooperated H/A/α decomposition with Wishart classifier for unsupervised PolSAR image classification. The symmetric revised Wishart (SRW) distance is proposed to construct a weighted graph for spectral clustering in [8]. The Wishart distance was used in the deep learning architecture, i.e., deep stacking network (DSN), named Wishart DSN for PolSAR image classification [11]. D is a diagonal matrix and its elements Dii = ∑j Gij. P can be the Laplacian matrix specific to a penalty graph Gp which characterizes the similarity properties which we try to constrain, or a diagonal matrix for scale normalization. P can be the Laplacian matrix specific to a penalty graph Gp which characterizes the similarity properties which we try to constrain, or a diagonal matrix for scale normalization. b is usually a constant
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