Semantic Annotation of High-Resolution Remote Sensing Images via Gaussian Process Multi-Instance Multilabel Learning

This letter presents a hierarchical semantic multi-instance multilabel learning (MIML) framework for high-resolution (HR) remote sensing image annotation via Gaussian process (GP). The proposed framework can not only represent the ambiguities between image contents and semantic labels but also model the hierarchical semantic relationships contained in HR remote sensing images. Moreover, it is flexible to incorporate prior knowledge in HR images into the GP framework which gives a quantitative interpretation of the MIML prediction problem in turn. Experiments carried out on a real HR remote sensing image data set validate that the proposed approach compares favorably to the state-of-the-art MIML methods.