Composite kernels conditional random fields for remote-sensing image classification

The problem of classifying a remote-sensing image by specifically labelling each pixel in the image is addressed. A novel method, named composite kernels conditional random field (CKCRF), which embeds multiple kernels into a classical CRFs model is proposed. Rather than manually selecting kernel-like KCRF, CKCRFs chooses the appropriate kernel by training. Moreover, a genetic programming-based decision-level fusion framework is proposed to tackle the problem of feature selection. It can select the appropriate features suitable to each category. Evaluations show that CKCRFs outperform CRFs and KCRFs, and CKCRFs with the fusion scheme is better than that without the fusion step.