A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification

Liu, Keng-Hao; Chen, Tsun-Yang

doi:10.3390/rs14225686

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…In [40], Zhu et al employed a structure-aware metric to evaluate the significance of all bands, and then selected representative bands by using dominant set extraction. Liu et al [41] proposed a band groupingbased sparse self-representation band selection method.…”

Section: Searching-based Methodsmentioning

confidence: 99%

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

Ning,

Wang

2023

IEEE Access

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Hyperspectral band selection plays a key role for overcoming the curse of dimensionality in the classification of hyperspectral remote sensing images (HSIs). Recently, clustering-based band selection methods have demonstrated great potential to select informative and representative bands for hyperspectral classification tasks. However, most clustering-based methods perform clustering directly on the original high-dimensional data, which reduces their performance. To address this problem, a novel band selection method called global-local consistency constrained deep embedded clustering (GLC-DEC) is proposed in this paper. In GLC-DEC, to simultaneously learn the lowdimensional embedded representation and cluster assignments of all bands in an HSI, the stacked autoencoder is integrated with the K-means method. In addition, to reduce the adverse impact of a limited number of training samples available in HSIs, local and global consistency constraints are imposed on the embedded representation so that discriminatively consistent representation of all bands is learned. Specifically, local graph regularization and global graph regularization are introduced into the GLC-DEC model, by which the strong correlation between neighboring bands and the manifold structure of all bands are fully exploited. Based on the clustering results provided by GLC-DEC, a group of representative bands are selected by using the minimum noise method. Experimental results on two real datasets demonstrate that the proposed GLC-DEC outperformed several state-of-the-art methods.

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Section: Searching-based Methodsmentioning

confidence: 99%

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

Ning,

Wang

2023

IEEE Access

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“…Thus, dimensionality reduction emerges as a critical step in hyperspectral data processing. Band selection (BS) [1][2][3][4][5] is an efficacious technique for diminishing the dimensionality of HSIs. This kind of method achieves dimensionality reduction by selecting the most valuable subset of bands from an HSI [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

Li,

Liu,

Hua

et al. 2023

Remote Sensing

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Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs). Recently, deep learning-based BS methods have received widespread interest due to their ability to model the nonlinear relationship between bands, with existing methods typically relying on generative algorithms. However, the process of generating images with pixel-level detail required by generative algorithm-based BS methods is computationally expensive. To alleviate this issue, we propose a contrastive learning-based unsupervised BS architecture, termed ContrastBS, in this article. With the help of contrastive learning, the proposed architecture avoids the costly generation step in pixel space by learning to distinguish data at the abstract semantic level of the feature space. Specifically, ContrastBS combines an attention mechanism with contrastive learning to extract the importance of each band. Furthermore, we design a novel loss function, which is able to constrain the symmetric loss while ensuring attention to the most valuable bands, for the contrastive learning-based BS network. Experimental results indicate that ContrastBS has excellent classification performance and competitive time cost compared to the comparison methods.

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SemanticFormer: Hyperspectral image classification via semantic transformer

Liu,

Wang,

Jiang

et al. 2024

Pattern Recognition Letters

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A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification

Cited by 5 publications

References 52 publications

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

SemanticFormer: Hyperspectral image classification via semantic transformer

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