Fast Spectral Clustering with Anchor Graph for Large Hyperspectral Images

The large-scale hyperspectral image (HSI) clustering problem has attracted significant attention in the field of remote sensing. Most traditional graph-based clustering methods still face challenges in the successful application of the large-scale HSI clustering problem mainly due to their high computational complexity. In this letter, we propose a novel approach, called fast spectral clustering with anchor graph (FSCAG), to efficiently deal with the large-scale HSI clustering problem. Specifically, we consider the spectral and spatial properties of HSI in the anchor graph construction. The proposed FSCAG algorithm first constructs anchor graph and then performs spectral analysis on the graph. With this, the computational complexity can be reduced to O(ndm), which is a significant improvement compared to conventional graph-based clustering methods that need at least O(n²d), where n, d, and m are the number of samples, features, and anchors, respectively. Several experiments are conducted to demonstrate the efficiency and effectiveness of the proposed FSCAG algorithm.