Fast Spectral Clustering via Efficient Multilayer Anchor Graph

Recent studies have shown that graph-based clustering methods are good at processing hyperspectral images (HSIs), while falling short for large-scale HSIs due to high time complexity. Meanwhile, the performance of these methods relies on the quality of the constructed graph with selected anchor points. More anchor points bring better clustering results for graph-based methods. Time complexity, however, sees a considerable increase as the number of anchor points grows. Therefore, a method that can obtain efficient clustering accuracy and consumes less time is to be developed. Against this backdrop, a novel algorithm named fast spectral clustering via efficient multilayer anchor graph (FEMAG) is proposed to resolve the accuracy and time-consuming trade-off problem. First, FEMAG adopts superpixel principal component analysis (SuperPCA) to extract the low-dimensional features of HSIs. Then, a multilayer anchor graph is constructed to improve the clustering performance. When constructing the similarity graph, FEMAG takes balanced K-means-based hierarchical K-means (BKHK) to obtain outperforming anchor points efficiently. Extensive experiments validate that FEMAG achieves better clustering accuracy while taking less time compared to previous clustering methods.