  Clustering of hyperspectral images is a fundamental but challenging task. The
recent development of hyperspectral image clustering has evolved from shallow
models to deep and achieved promising results in many benchmark datasets.
However, their poor scalability, robustness, and generalization ability, mainly
resulting from their offline clustering scenarios, greatly limit their
application to large-scale hyperspectral data. To circumvent these problems, we
present a scalable deep online clustering model, named Spectral-Spatial
Contrastive Clustering (SSCC), based on self-supervised learning. Specifically,
we exploit a symmetric twin neural network comprised of a projection head with
a dimensionality of the cluster number to conduct dual contrastive learning
from a spectral-spatial augmentation pool. We define the objective function by
implicitly encouraging within-cluster similarity and reducing between-cluster
redundancy. The resulting approach is trained in an end-to-end fashion by
batch-wise optimization, making it robust in large-scale data and resulting in
good generalization ability for unseen data. Extensive experiments on three
hyperspectral image benchmarks demonstrate the effectiveness of our approach
and show that we advance the state-of-the-art approaches by large margins.
