Local and Nonlocal Context-Aware Elastic Net Representation-Based Classification for Hyperspectral Images

By representing a query sample as a linear combination of all labeled samples and then classifying it by evaluating which class leads to the minimal representation error, the representation-based classification methods have been successfully used for the classification of hyperspectral images (HSI). According to the usage of different norms, the sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) methods have been presented in two different paradigms. The SRC promotes the use of few labeled samples, while the CRC encourages the use of all labeled samples to collaboratively represent the query one from all classes. However, when the limited labeled samples of different classes are unbalance, the learnt representation is hardly to reflect the particular characteristics of each class. To overcome this problem, this paper presents a novel graph based context-aware elastic net (ELN) model for the HSI classification. Under a generalized ELN framework, the proposed model is able to take full advantages of SRC and CRC. Specifically, by evaluating the spectral and spatial self-similarity of local and nonlocal neighbors, an ELN-coding neighborhood graph is constructed with image patch distance. Owing to the exploitation of the spectral-spatial context, a centralized sparsity norm is integrated into the optimization model and it can promote the local and global consistence preserving. Finally, an efficient solver for the proposed model is developed by using the well-known alternating direction method of multiplier. Experiments on several real datasets validated that the proposed method can outperform state-of-the-art algorithms in terms of classification accuracy. Furthermore, even with the limited unbalanced labeled samples the proposed method is robust.