A novel approach to multispectral blind image fusion

In this paper we propose a robust method of data fusion for the classification of multispectral images. The approach is novel in that it attempts to remove blurring of the images in conjunction with fusing the data. This produces a more robust and accurate overall classification scheme. The approach is applicable to situations in which registered multispectral images of the same scene are available. The novel scheme is comprised of three main stages. The first stage involves the blind restoration of the degraded multispectral images to combat blurring effects. The results are fused in the second stage with a statistical classification method which performs both pixel-level and intermediate-level classification. The classification output is then passed through a final stage which provides a relative measure of the success of the classification method. This information is fed back to the first stage to improve the reliability of the restoration method. The performance of the proposed scheme is demonstrated by applying the technique to simulated and real photographic data. The simulation results demonstrate the potential of the method for robust classification of degraded data.