Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image
  Clustering
Jingxin Wang Renxiang Guan Kainan Gao Zihao Li Hao Li Xianju Li Chang Tang
Hyperspectral image (HSI) clustering is a challenging task due to its high
complexity. Despite subspace clustering shows impressive performance for HSI,
traditional methods tend to ignore the global-local interaction in HSI data. In
this study, we proposed a multi-level graph subspace contrastive learning
(MLGSC) for HSI clustering. The model is divided into the following main parts.
Graph convolution subspace construction: utilizing spectral and texture
feautures to construct two graph convolution views. Local-global graph
representation: local graph representations were obtained by step-by-step
convolutions and a more representative global graph representation was obtained
using an attention-based pooling strategy. Multi-level graph subspace
contrastive learning: multi-level contrastive learning was conducted to obtain
local-global joint graph representations, to improve the consistency of the
positive samples between views, and to obtain more robust graph embeddings.
Specifically, graph-level contrastive learning is used to better learn global
representations of HSI data. Node-level intra-view and inter-view contrastive
learning is designed to learn joint representations of local regions of HSI.
The proposed model is evaluated on four popular HSI datasets: Indian Pines,
Pavia University, Houston, and Xu Zhou. The overall accuracies are 97.75%,
99.96%, 92.28%, and 95.73%, which significantly outperforms the current
state-of-the-art clustering methods.