One-Class Classification of Remote Sensing Images Using Kernel Sparse Representation

Sparse representations have been widely studied in remote sensing image analysis in recent years. In this paper, we develop a novel method for one-class classification (OCC) using a kernel sparse representation model for remotely sensed imagery. Training samples taken from the target class alone are used to build a learning dictionary for the sparse representation model, which is then optimized to produce a reconstruction residual. In the proposed model, a pixel is classified as the target class if the obtained reconstruction residual for the pixel is smaller than a given threshold; otherwise, the pixel is labeled as the outlier class. To improve the data separability between the target and outliner classes, the training samples taken from the target class are mapped into a high-dimensional feature space using a kernel function to build a learning dictionary for the kernel sparse representation model. OCC is then conducted in the mapped high-dimensional feature space using the reconstruction residual threshold, following the same principle as OCC in the original feature space. The proposed OCC method is evaluated and compared with several existing OCC methods in three different case studies. The experimental results indicate that the proposed method outperforms these existing methods, particularly when using a kernel sparse representation.