Hyperspectral Image Clustering based on Variational Expectation Maximization

Jiao, Yuchen; Ma, Yirong; Gu, Yuantao

doi:10.1109/sam48682.2020.9104375

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Traditional clustering methods of HSI include centroid-based [19][20][21], densitybased [22][23][24], probability-based [25][26][27], and biologically driven methods [28,29]. Modelbased optimization methods [30][31][32][33][34] that employ matrix representation techniques, such as sparse representation (SR) [35], low-rank representation (LRR) [30], and non-negative matrix factorization (NMF) [36], have achieved the current state-of-the-art performance, attracting significant attentions in the fields.…”

Section: Building Watermentioning

confidence: 99%

From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

2023

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Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.

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