Unsupervised High-Level Feature Extraction of SAR Imagery With Structured Sparsity Priors and Incremental Dictionary Learning

Sparse representation is an effective model for high-level feature extraction, and the dictionary is critical, since it can provide a sparse and discriminative feature for image classification. However, the traditional sparse model with ℓ
₁
- norm is unstable and ignores spatial context dependence. Furthermore, the traditional off-line dictionary learning is less efficient. In this letter, a high-level feature extraction approach is proposed, in which structured sparsity priors are imposed on the sparse representation to exploit the context dependence and an incremental structured dictionary learning method is proposed to exploit the inherent structures of a dictionary. The experiment results on unsupervised synthetic aperture radar imagery classification show that the structured priors improve classification performance and the proposed algorithm is more efficient in dictionary learning compared with existing works.