Block Diagonal Representation Learning for Hyperspectral Band Selection

Li, Shuying; Liu, Zhe; Fang, Long; Li, Qiang

doi:10.1109/tgrs.2023.3266811

Cited by 4 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly used methods for HSI dimensionality reduction to reduce spectral band redundancy include band selection (BS) [6,7] and feature extraction (FE) [8,9], where BS-based methods are based on the criteria of selecting the most representative band subset directly from the original HSI data without any transformation, and the obtained sub-bands are informative, distinguishable, and beneficial for subsequent tasks. Compared with FE, BS can reduce data dimensionality while preserving the physical meaning, inherent properties, and spectral characteristics of the original data, which is beneficial for interpreting the selected subset of bands in subsequent analysis and has been widely used in practical applications [10,11].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

Wang,

Ma,

Yang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of attention with good effect. However, many of them lack the self-supervision of representations and ignore the multi-level spectral–spatial information of HSI and the connectivity of subspaces. To this end, this paper proposes a novel self-supervised multi-level representation learning fusion-based maximum entropy subspace clustering (MLRLFMESC) method for hyperspectral band selection. Firstly, to learn multi-level spectral–spatial information, self-representation subspace clustering is embedded between the encoder layers of the deep-stacked convolutional autoencoder and its corresponding decoder layers, respectively, as multiple fully connected layers to achieve multi-level representation learning (MLRL). A new auxiliary task is constructed for multi-level representation learning and multi-level self-supervised training to improve its capability of representation. Then, a fusion model is designed to fuse the multi-level spectral–spatial information to obtain a more distinctive coefficient matrix for self-expression, where the maximum entropy regularization (MER) method is employed to promote connectivity and the uniform dense distribution of band elements in each subspace. Finally, subspace clustering is conducted to obtain the final band subset. Experiments have been conducted on three hyperspectral datasets, and the corresponding results show that the proposed MLRLFMESC algorithm significantly outperforms several other band selection methods in classification performance.

show abstract