Anew Pansharpening Method with Multi-Scale Structure Perception

Yu Pan, Xu Li, Ang Gao, Lixin Li, Shaohui Mei, Shigang Yue

Abstract: The remote sensing images provided by satellites usually contain complex earth objects with different scales. In the fusion of such images, most of the existing filtering-based pansharpening methods often suffer from spectral and/or spatial information distortions due to the inaccuracy of the detail extraction. Motivated by this, we propose an effective and straightforward multi-scale structure perception pansharpening method, which uses the structure-preserving filter with great structure-aware ability to progressively perceive the structures and accurately extract the details. The experiment is carried out on GeoEye-1 satellite images. Visual and objective analysis show that our method can produce high-quality pansharpened results and outperform some existing methods.