Improving spatiotemporal reflectance fusion using image inpainting and steering kernel regression techniques

ABSTRACT

A novel spatiotemporal reflectance fusion method integrating image inpainting and steering kernel regression fusion model (ISKRFM) is proposed to improve the fusion accuracy for remote-sensing images with different temporal and spatial characteristics in this article. This method first detects the land-cover changed regions and then fills them with unchanged similar pixels by an exemplar-based inpainting technique. Furthermore, a steering kernel regression (SKR) is used to adaptively determine the weightings of local neighbouring pixels to predict high spatial resolution image. Accordingly, the main contributions of this method are twofold. One is to address the land-cover change issues in the spatiotemporal fusion, and the other is to establish an adaptive weighting assignment according to the pixel locations and the radiometric properties of the local neighbours to account for the effect of neighbouring pixels. To validate the proposed method, two actual Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions at southeast China were implemented and compared with the baseline spatial and temporal adaptive reflectance fusion model (STARFM). The experimental results demonstrate that addressing the land-cover changes in spatiotemporal fusion has positive effects on the fused image, and the proposed ISKRFM method significantly outperforms STARFM in terms of both visual and quantitative measurements.

Acknowledgements

This research was funded by the Natural Science Foundation of China (No. 41571330, 41401488), the Natural Science Foundation of Fujian province (No. 2015J01163), and Fujian Collaborative Innovation Center for Big Data Application in Governments (No. 2015750401).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is was funded by the Natural Science Foundation of China (No. 41571330, 41401488), the Natural Science Foundation of Fujian province (No. 2015J01163), and Fujian Collaborative Innovation Center for Big Data Application in Governments (No. 2015750401).