Fast Linear Unmixing of Hyperspectral Image by Slow Feature Analysis and Simplex Volume Ratio Approach

This paper proposes a novel, faster unmixing approach based on a convex geometric approach and slow feature analysis, and extends it to perform efficient library pruning based semi-blind unmixing. The former algorithm performs complete blind unmixing, whereas the subsequent algorithm performs exact library pruning for semi-blind unmixing. Slow feature analysis algorithm impels the pure pixels towards the exterior region. The work identifies the endmembers by detecting the extreme points. On the other hand, the proposed dictionary pruning method augments each library element with the data, extracts the extreme points and calculate a volume of the transformed data. The augmentation of actual image endmember changes the structure of the simplex. The library pruning method proposes an index to capture the change in the volume of the simplex and identify the actual image endmember. We evaluated unmixing performance as well as the runtime on real images, which ratifies the computational edge and proficiency of our proposed method.