Semisupervised hyperspectral image classification based on affinity scoring

There are two great challenges for classification of hyperspectral images (HSIs): lack in prior knowledge and serious internal-class variability. To address the issues, we propose a novel semisupervised method based on affinity scoring (AS). It can harness the fuzzy state of the contributions of spectral and spatial features to classification. The method consists of three major steps: over-segmentation, semisupervised classification and modification. First, superpixels are generated to maintain local class consistency, which can balance spectral variability. Then unlabeled samples are classified by AS in an iterative manner, whereas precious labeled samples are made most use of. Finally, AS is adopted again to refine the classification map, which further exploits spatial smoothness in HSIs. Experiments show that the proposed method can largely outperform several state-of-the-art classifiers.