Evaluation of Image Fusion Methods Applied to the ZY1-04 Data of Chengdu Area

Different remote sensing datasets have considerably heterogeneous natures. To utilize multisource data, image fusion has been widely researched as an effective and efficient processing approach in remote sensing applications. One of the most challenging problems is how to obtain images with higher spatial and spectral resolution through pan-sharpening. It is also an unsolved problem in the application of domestically-produced satellites which have been developed rapidly in recent years. To find an appropriate fusion method for the ZY1-04 data of Chengdu area, we analyzed four representative algorithms: GramSchmidt, Pansharp, NNDiffuse and SFIM. Image preprocessing was carefully done and the fundamental steps were analyzed. Performance assessment of the fusion algorithms was given both qualitatively and quantitatively. The result suggests that GramSchmidt algorithm generally has the best overall performance for the ZY1-04 data, while other methods are still distinctive in certain aspects.