Large-Scale Hyperspectral Image-Projected Clustering via Doubly Stochastic Graph Learning

Hyperspectral image (HSI) clustering has drawn more and more attention in recent years as it frees us from labor-intensive manual annotation. However, current works cannot fully enjoy the rich spatial and spectral information due to redundant spectral signatures and fixed anchor learning. Moreover, the learned graph always obtains suboptimal results due to the separate affinity estimation and graph symmetry. To address the above challenges, in this paper, we propose large-scale hyperspectral image-projected clustering via doubly stochastic graph learning (HPCDL). Our HPCDL is a unified framework that learns a projected space to capture useful spectral information, simultaneously learning a pixel–anchor graph and an anchor–anchor graph. The doubly stochastic constraints are conducted to learn an anchor–anchor graph with strict probabilistic affinity, directly providing anchor cluster indicators via connectivity. Meanwhile, when using label propagation, pixel-level clustering results are obtained. An efficient optimization strategy is proposed to solve our HPCDL model, requiring monomial linear complexity concerning the number of pixels. Therefore, our HPCDL has the ability to deal with large-scale HSI datasets. Experiments on three datasets demonstrate the superiority of our HPCDL for both clustering performance and the time burden.