LAUSR

Conditional random fields (CRFs) model is suitable for image segmentation because it can capture the dependencies of observed data and incorporate the spatial correlations into the segmentation process. In this article, to deal with the segmentation of nonstationary synthetic aperture radar (SAR) image, we combine the modeling power of the CRF model with the representation-learning ability of principal component analysis network (PCANet), and thus propose a high-order triplet CRF model based on PCANet (HOTCRF-PCANet). HOTCRF-PCANet introduces an auxiliary field to explicitly regulate nonstationary label structure patterns. Under the guidance of this auxiliary field, HOTCRF-PCANet defines a discrete quadrilateral nonstationary Markov fields model, and thus considers both the nonstationary property of image and high-order label interactions. In addition, guided by the auxiliary field, HOTCRF-PCANet proposes to use a product-of-expert (POE) potential to enforce the regions’ labeling consistency for pixels within the weak-structured region. To automatically learn rich feature representations, HOTCRF-PCANet modifies PCANet into an unsupervised mode, i.e., unsupervised PCANet (UPCANet), and constructs an UPCANet-based unary potential to effectively predict the local class probability. The effectiveness of HOTCRF-PCANet is demonstrated by the application to the unsupervised segmentation of the simulated images and real SAR images.