Pansharpening with a decision fusion based on the local size information

Pan sharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pan sharpening methods produce images with different characteristics. In the 2006 IEEE Data Fusion Contest, À-trous Wavelet Transform based pansharpening (AWLP) and Context Adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, it was observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to develop a method which could produce results locally approximately similar to the best method, among the two pansharpening methods. In this paper we propose a method which selects either of the two methods for performing pansharpening on local regions, based upon the size of the objects. The results obtained demonstrate that the proposed method produces images with quantitative results approximately similar to the method which is better among the AWLP and CBD pansharpening methods.