---
title: "Robust Federated Hyperspectral Image Clustering"
authors:
  - "Xiang Yang"
  - "Zhengzhong Zhu"
  - "Dayu Hu"
  - "Xiaowen Ma"
  - "Zihao Li"
  - "Wenxuan Tu"
  - "Taichun Zhou"
  - "Wenxin Zhang"
  - "Renxiang Guan"
doi: "10.24963/ijcai.2026/579"
url: "https://ijcai.org/proceedings/2026/579"
pdf: "https://ijcai.org/proceedings/2026/0579.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5200-5208"
track: "Main Track"
keywords:
  - "Machine Learning: Clustering"
---

# Robust Federated Hyperspectral Image Clustering

Xiang Yang, Zhengzhong Zhu, Dayu Hu, Xiaowen Ma, Zihao Li, Wenxuan Tu, Taichun Zhou, Wenxin Zhang, Renxiang Guan

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5200-5208. DOI: [10.24963/ijcai.2026/579](https://doi.org/10.24963/ijcai.2026/579)

## Abstract

Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physical distribution of data across isolated silos. While Federated Learning offers a decentralized solution, existing frameworks in remote sensing are predominantly confined to supervised paradigms and struggle to address the heavy reliance on annotations and Non-IID distributions inherent among clients. To overcome these limitations, we propose a novel framework named Robust Federated HSI Clustering(RFHC) that enables collaborative unsupervised learning without requiring raw data exchange. Specifically, we design a dual-encoder architecture that incorporates a Federated Model Weight Augmentation strategy(FMWA), which generates consistent views through local-global network interactions to mitigate the spectral distortion introduced by traditional data augmentation. Furthermore, we develop a hybrid optimization objective that synergizes prototype relationship optimization with contrastive learning. This mechanism utilizes global prototypes to guide local training, effectively stabilizing feature learning against data heterogeneity while ensuring intra-cluster compactness. Extensive experiments on three benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed method against state-of-the-art model(SOTA) federated approaches.