Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
This work addresses the problem of high complexity in hyperspectral image clustering for remote sensing applications, representing an incremental improvement by combining existing techniques like graph convolution and contrastive learning.
The paper tackles hyperspectral image clustering by proposing a multi-level graph subspace contrastive learning model that integrates global-local interactions, achieving overall accuracies of 97.75%, 99.96%, 92.28%, and 95.73% on four datasets, significantly outperforming state-of-the-art methods.
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.