Adaptive based method for multispectral image pansharpening: Application to Quickbird data sets

In remote sensing, satellite images acquired from sensors provide either high spectral or spatial resolutions. The pansharpening framework is applied on remote sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) image using information from panchromatic image. A multi-decomposition pansharpening approach combining MS and PAN images is proposed in this paper in order to upscale the low-resolution MS image so to match the panchromatic (PAN) resolution. Particularly, multi-level wavelet decomposition is applied on YUV space transformation or Extended YUV transformation taking into account the near infrared (NIR) channel of the original MS channels, where geometrical details from the PAN image are introduced in the MS ones. After that, the fusion approach is applied, and compared to the original approach where the details of the fused MS´ luminance (Y) are put equal the PAN ones, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between Y and lowed PAN image is taken. Indeed, high resolution geometrical details cannot be similar if the low resolution details are not in good agreement. Finally, the evaluation is done based on QuickBird data sets showing rural and suburban areas. Compared to recent methods, our approach provides better results.