A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection

ABSTRACT

This article presents a novel semi-supervised change detection approach for very-high-resolution (VHR) remote-sensing images. The proposed approach aims at extracting the change information by making full use of the context-sensitive relationships among pixels in the images. This is accomplished via a context-sensitive image representation technique based on hypergraph model. First, each temporal image is modelled as a hypergraph that utilizes a set of hyperedges to capture the context-sensitive properties of pixels in the image. Second, the difference in the bi-temporal images is measured by both the similarity and the consistency between the two hypergraphs. Finally, the changes are separated from the unchanged ones by a hypergraph-based semi-supervised classifier on the difference image. Experimental results obtained on different VHR remote-sensing data sets demonstrate the effectiveness of the proposed approach.

Acknowledgements

The authors would like to thank the editor and referees for their helpful suggestions which significantly improved the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

P. Jian was supported by the National Natural Science Foundation of China [grant number 61202244], [grant number 61502259] and K. Chen was supported by the National Natural Science Foundation of China [grant number 61302170].