Automatic semantic annotation of high-resolution optical satellite images is a task to assign one or several predefined semantic concepts to an image according to its content. The fundamental challenge arises from the difficulty of characterizing complex and ambiguous contents of the satellite images. To address this challenge, a diversity constrained joint multi-feature learning method is proposed to learn robust feature representations for annotating satellite images. The key motivation of our method is to make full use of the complementarity diversity information among the heterogeneous features in the learning process. Comprehensive experiments on an annotation dataset demonstrate the superiority and effectiveness of our method compared with baseline multi-feature learning method.
['Xiwen Yao', 'Junwei Han', 'Gong Cheng', 'Peicheng Zhou', 'Lei Guo']
Semantic annotation of satellite images via joint multi-feature learning with diversity constraint