The Study Of Remote Sensing Image Fusion Algorithm And Evaluation Of Effect

As an effective mean of remote sensing image analysis, Remote sensing imagefusion technology has been a rapid development in recent years. Presently althoughthere are many classical fusion algorithms, the results of these fusion algorithms tosome extent appear spectral distortion; tone variation etc.One of the hot issues of theremote sensing field is how to make the fusion results not only with higher spatialresolution but also with lower spectral distortion.The best band combination of fusion images is finally determined by consideringthe average value, standard deviation, covariance matrix, correlation coefficient matrix,OIF index. Using the traditional remote sensing image fusion algorithms, includingHSV, PCA, Brovey, Gram-Schmidt, SFIM and IHS transform, to process the fusionimages. Through evaluating results of various fusions, we can find that the fusionresults of the traditional IHS transform appear more serious spectral distortion althoughthey have significantly higher spatial resolution than others. In order to improving thedeficiency of single algorithm, the traditional methods mostly combine variousalgorithms, which will lead to the fusion process relatively complex, and not conduciveto the actual operation.Therefore, this paper proposes an improved IHS transform based on local varianceweighted. To begin with, the best local variance images are obtained by analyzing thedifferent sizes of sliding window, those images are normalized subsequently. Then,SPOT image process histogram matching according to the I component. The weightrelations between the local variance image and the I component and SPOT image areestablished by using different power functions. Finally, through the comprehensiveanalysis of standard deviation, average gradient, entropy, degree of distortion, differencecoefficient, and correlation coefficient of fusion results, the most cost power exponentcan be identified, which can be used to obtain the fusion images with high spatialresolution and high spectral fidelity. This algorithm can be used as a good model forremote sensing image fusion.