Resolution enhancement of hyperspectral images using distortion optimization

Pansharpening algorithms have been widely discussed since the poor spatial resolution of the hyperspectral images limits their use in identifying textures or some other detailed information of different ground types in many applications such as sophisticated interpretation. To overcome the defect is of the top priority in this topic, for the conventional pansharpening methodologies usually suffer both spectral and spatial distortion severely after the processing. In this paper, a hyperspectral images resolution enhancement method aiming at distortion optimization is presented. Quality-based Segmented Principal Component Transform (QSPCT) is proposed to minimize the distortion, after which the injection model is employed. The potential of the proposed method is further demonstrated through comparative analysis against a number of standard pan-sharpening algorithms on EO-1 data sets.