期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3127536
关键词
Clustering; hyperspectral images; subspace representation
类别
资金
- Flanders AI Research Programme [174B09119]
- Bijzonder Onderzoeksfonds (BOF) [BOF.24Y.2021.0049.01]
- National Natural Science Foundation of China [61871298, 42071322]
The authors propose a scalable subspace clustering method in this article, which reduces significantly the size of optimization problems by learning a concise dictionary and robust subspace representation. The method also introduces an adaptive spatial regularization to improve the robustness to noise.
Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input HSI data as a dictionary of atoms, in terms of which all the data samples are linearly represented. This leads to highly redundant dictionaries of huge size, and the computational complexity of the resulting optimization problems becomes prohibitive for large-scale data. In this article, we propose a scalable subspace clustering method, which integrates the learning of a concise dictionary and robust subspace representation in a unified model. This reduces significantly the size of the involved optimization problems. We introduce a new adaptive spatial regularization for the representation coefficients, which incorporates spatial information of HSI and improves the robustness of the model to noise. We derive an effective solver based on alternating minimization and alternating direction method of multipliers (ADMMs) to solve the resulting optimization problem. Experimental results on four representative hyperspectral images show the effectiveness of the proposed method and excellent clustering performance relative to the state of the art.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据