Built-Up Area Detection From Satellite Images Using Multikernel Learning, Multifield Integrating, and Multihypothesis Voting

This letter proposes a novel supervised approach for accurate built-up area detection from high-resolution remote sensing images. In existing supervised built-up area detection approaches based on block-based image interpretation, the determination of the block size and the pursuit of the pixel-level result are not well addressed. Concerning these issues, this letter proposes a complete and systematic approach. It first utilizes multikernel learning to incorporate multiple features to implement the block-level image interpretation. Then, multifield integrating (i.e., the image interpretation results using different block sizes are fused) is proposed to obtain the block-level result. On the basis of the achieved result of the second step, multihypothesis voting is finally presented for working toward the pixel-level built-up area detection result through multihypothesis superpixel representation and graph smoothing. The proposed approach has been validated in the ZY-3 and GF-1 satellite images, and experimental results show that the proposed approach can outperform the state-of-the-art approaches.