Semisupervised Spectral–Spatial Classification of Hyperspectral Imagery With Affinity Scoring

Semi supervised classification has become popular, since it can make use of a limited amount of prior knowledge in hyperspectral images. However, spectral internal-class variability adds a great challenge to the task. To address these issues, we propose a novel semi supervised spectral-spatial classification method based on affinity scoring (AS) (SCAS). Adapted from fuzzy logic, AS exploits spectral and spatial features with their fuzzy contributions to classification by weighing on three factors: local class consistency, spectral similarity, and prior knowledge. SCAS consists of three main steps: oversegmentation, semi supervised classification, and modification. The first step generates super pixels and uses them to maintain local class consistency. The second and third steps employ AS to classify the super pixels and refine the classified map, respectively. Experiments show that the proposed method can outperform some classic methods and state-of-the-art classifiers.