Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

ABSTRACT

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.

Acknowledgements

The work is supported by the National Natural Science Foundation of China [grant number 61275010], Ph.D. Programs Foundation of Ministry of Education of China [grant number 20132304110007], the Heilongjiang Natural Science Foundation [grant number F201409], and the Fundamental Research Funds for the Central Universities [grant number HEUCFD1410]. The authors would like to thank the handling editors and the reviewers for providing valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work is supported by the National Natural Science Foundation of China [grant number 61275010], Ph.D. Programs Foundation of Ministry of Education of China [grant number 20132304110007], the Natural Science Foundation of Heilongjiang Province [grant number F201409], and the Fundamental Research Funds for the Central Universities [grant number HEUCFD1410].