Superresolution of Single Gaofen-4 Visible-Light and Near-Infrared (VNIR) Image Based on Texture Image Extraction

Mi Wang, Luxiao He, Xueli Chang, Yufeng Cheng

Abstract: Superresolution (SR) is an important compensatory mean for geostationary orbit (GEO) satellites because of their low spatial resolution caused by orbital height. The multiple-image SR (MISR) methods generally have better reconstruction effects than single-image SR (SISR) methods. However, MISR methods require multiple images of the same place, which are susceptible to geometric distortion and radiation differences. Gaofen-4 (GF-4) is a GEO satellite equipped with a visible-light and near-infrared (VNIR) sensor and provides images with five bands. Based on the similarity of spatial information between different bands, this paper proposes an SISR method suitable for GF-4 VNIR images. The radiation differences are eliminated by extracting texture images. The high-resolution (HR) texture image is reconstructed by projections onto convex sets. The final SR result is fused by HR texture image and upsampled VNIR image. Tested by real GF-4 data, the reconstructed results of this method have better sharpness and more abundant information than comparable methods.