A new pansharpening method using multi resolution analysis framework and deep neural networks

Present work describes a promising method in image fusion remote sensing applications. Due to intrinsic properties of deep neural networks (DNN) in image reconstruction, a novel pansharpening method presents based on multi resolution analysis (MRA) framework. First, a low resolution Panchromatic (LR Pan) image is constructed using its high resolution (HR) version. Then, the relationship between LR/HR Pan images are used to reconstruct the HR Multispectral (MS) image utilizing the LR MS. For our work, two datasets are considered and for each of them, the effect of several parameters such as window size, overlapping percentage and number of training samples on spectral distortion are considered. After training DNN, the LR MS image is given to the trained network as input to obtain MS image with better spatial details and finally the fused image obtains using MRA framework. Comparison with state of art methods, the proposed method has better results from objective and visual perspectives.