Building Extraction From Remote Sensing Image With Privileged Information

Abstract

Building extraction using additional information such as height information has been paid more attention, due to its promising performance. However, additional information is not always easy to collect and may be not available at test stage in practice. Existing building extraction methods using additional information will fail, due to the lack of clear strategies to deal with this situation. This paper proposes a novel multiple kernel SVM+ (MK-SVM+) method to fully exploit additional information (referred as privileged information) which is only available at the training stage. MK-SVM+ simultaneously learns optimal adaptive combined kernels using multiple different base kernels, and builds a new SVM+ model using privileged information. As a result, the derived MK-SVM+ method has more discriminative ability for building extraction. Performance evaluations on a real-world dataset show that our method outperforms compared methods and demonstrate the effectiveness of the proposed method.