A Spatiotemporal Fusion Algorithm of Remote Sensing Images Based on Cross-Scale Similarity Prior
The trade-off between spatial and temporal resolution of satellite images leads to spatial and temporal con-tradictions in image sequences. Spatiotemporal image fusion provides a solution to generate high spatial resolution and high temporal resolution images to satisfy various earth observation applications. The spatiotemporal fusion algorithm based on sparse representation establishes the relationship between high and low spatial resolution images by jointly training the dic-tionary and sparse coding representation,which provides a unified fusion framework for phenological change and type change. However,the multi-source remote sensing images come from different sensors,and the relationship model between high and low spatial resolution images implies the sensor mapping. This inevitably leads to that the model is device depen-dent. To solve the problem,we decompose the multi-source remote sensing spatiotemporal fusion process into two sub-problems,device dependent sensor bias correction and device independent spatiotemporal fusion. The sensor bias correction can be used as a preprocessing module to improve the universality and accuracy of subsequent fusion models. When there are large space scale gaps between high and low spatial resolution image,the assumption that "the sparse coefficients of high and low spatial resolution images are the same" will bring about very significant fusion errors. To solve the problem,we optimize the objective function of sparse representation using cross-scale similarity prior. Intermediate-scale images are constructed to reduce ambiguity of cross-scale similar patches and improve the accuracy of cross-scale similar patches. Ex-perimental results in three typical scenarios demonstrate the generalization ability of our algorithm. The contrastive experi-ments show that on the BOREAS dataset,compared to suboptimal indicators,SSIM (Structural SIMilarity) is improved by 4.2％,SAM (Spectral Angle Mapper) is increased by 4.6％;On the CIA dataset,compared to suboptimal indicators,SSIM is increased by 2.7％,and SAM is increased by 12.8％;On the LGC dataset,compared to suboptimal indicators,SSIM is in-creased by 7.1％,and SAM is increased by 16.3％. Our algorithm is superior to other compared methods in spatial and spec-tral performance.
万方数据
维普
