Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring

Recently, a general framework for spectral-spatial classification has caught the attention of the hyperspectral imagery(HSI) society. It consists of three parts: classification, segmentation and combination of the former results to make a refined labeled map. Seeing the potentials of the last part, we derive a novel combination rule based on affinity scoring(CRAS). The core of the system is affinity score(AS), which is derived from fuzzy logic. Every AS measures the degree, i.e., the affinity, by which a pixel belongs to a class. The score is essentially decided by three factors: local spatial consistency, spectral similarity, and prior knowledge. The method is compatible with basic classification and segmentation tools, thus saving the trouble of designing complex techniques for the other parts in the framework. Experimental results show that CRAS excels several basic techniques as well as various state-of-the-art methods in the area of spectral-spatial classification.