ABSTRACT
Remote sensing images are widely used for different areas ranging from mineral exploration to agricultural applications, but the poor quality of hyperspectral images will directly have an adverse effect on these applications. Remotely sensed image pan-sharpening techniques combine a high-spatial–low-spectral–resolution image and a low-spatial–high-spectral–resolution image to attain high-quality images. The need for image pan-sharpening methods arises from the fact that satellite sensors face technical difficulties in capturing high-spatial resolution multispectral images (MS) images. The need for image pan-sharpening methods arises from the fact that satellite sensors face technical difficulties in capturing high-spatial resolution MS images. Nonsubsampled Contourlet Transform is an extension of the well-known Contourlet Transform (CT). The multiscale decomposition feature of CT is achieved by using Laplacian Pyramids. Optimistic conservation of more informative blocks from low-resolution remote sensing images from the same scene into one high-resolution image is referred to as pan-sharpening.
