S3LBI: Spectral–Spatial Segmentation-Based Local Bicubic Interpolation for Single Hyperspectral Image Super-Resolution

Abstract

Single hyperspectral image (HSI) super-resolution (SR), which is limited by the lack of exterior information, has always been a challenging task. A lot of effort has gone into fully mining spectral information or adopting pretrained models to enhance spatial resolution. However, few SR approaches take into account structural features from the perspective of multidimensional segmentation of the image. Therefore, a novel spectral–spatial segmentation-based local bicubic interpolation (S3LBI) is proposed to implement segmented and blocked interpolation according to the characteristics of HSI. Specifically, the bands of an HSI are clustered into several spectral segments. Then, super-pixel segmentation is carried out in each spectral segment. After that, the bicubic interpolations are separately conducted on different spectral–spatial segments. Experiments demonstrate the superiority of our S3LBI over the compared HSI SR approaches.