Pan-sharpening of spectral image with anisotropic diffusion for fine feature extraction using GPU

Feature extraction from satellite imagery is a challenging topic. Commercial multispectral satellite data sets, such as WorldView 2 images, are often delivered with a high spatial resolution panchromatic image (PAN) as well as a corresponding low-resolution multispectral spectral image (MSI). Certain fine features are only visible on the PAN but difficult to discern on the MSI. To fully utilize the high spatial resolution of the PAN and the rich spectral information from the MSI, a pan sharpening process can be carried out. In this paper, we propose a novel and fast pan sharpening process based on anisotropic diffusion with the aim to aid feature extraction that enhances salient spatial features. Our approach assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring superpixels; it treats spectrum as its smallest element of operation, which is different from most existing algorithms that process each band separately. Our approach is shown to be capable of preserving salient features. In addition, the process is highly parallel with intensive neighbor operations and is implemented on a general purpose GPU card with NVIDIA CUDA architecture that achieves approximately 25 times speedup for our setup. We expect this algorithm to facilitate fine feature extraction from satellite images.