Structured-Anchor Projected Clustering for Hyperspectral Images

Hyperspectral image (HSI) clustering seeks to assign each pixel to a specific class without trained labels. This is a challenging task owing to the spatial and spectral complexity. Recently, anchor graph-based clustering has attracted considerable attention due to its flexibility in handling large-scale HSI data. However, these methods typically disregard noisy bands and require post-processing. To tackle these issues, we propose a structured-anchor projected clustering (SAPC) model for HSIs. In SAPC, the projection clustering is introduced into anchor graph learning to suppress noise, and the Laplacian rank constraints can quickly obtain the structure of anchors. Thus, we can directly obtain the clustering results through the anchor graph and the structured anchors. Moreover, we propose an iterative optimization method to efficiently solve the SAPC model. Extensive experiments show that our model achieves superior results.