Super-resolution of hyperspectral images

Abstract

Hyperspectral (HS) images are used for space areal application, target detection and remote sensing application. HS images are very rich in spectral resolution but at a cost of spatial resolution. HS images generated by airborne sensors like the NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) from satellites like NASA’s Hyperion. We proposed a principal component analysis (PCA) based learning method to increase a spatial resolution of HS images. For spatial resolution enhancement of HS images we need to employ a technique to increase the resolution. We used PCA based approach by learning the details from database which consist of high spatial resolution satellite images. Super-resolution, is an ill-posed problem, and does not result to unique solution, and therefore it is necessary to regularize the solution by imposing some additional constraint to restrict the solution space. To reduce the computational complexity, minimization of the regularized cost function is done using the iterative gradient descent algorithm. In this report the effectiveness of proposed scheme is demonstrated by conducting experiments on both Multispectral (MS) and Hyperspectral real data. The HS and MS images of AVIRIS and Digital airborne Imaging spectrometer (DAIS) respectively used as input for super resolution (SR).