Representativeness and Redundancy-Based Band Selection for Hyperspectral Image Classification`

Liu, Yuanyuan; Li, Xiaorun; Feng, Yanghe; Zhao, Liaoying; Zhang, Wenqiang

doi:10.1080/01431161.2021.1875511

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…In point-wise approaches, the selected subset of bands is obtained by appending or eliminating bands one at a time. Examples of point-wise approaches are representativeness and redundancy-based BS (RRBS) [13] and orthogonal-projection-based BS (OPBS) [14]. Ranking-based approaches, such as similarity-based ranking structural similarity (SR-SSIM) [8], linearly constraint minimum variance (LCMV) [15], exemplar component analysis (ECA) [16], and maximum-variance principal component analysis (MVPCA) [17] employ certain criteria to rank all bands and subsequently select the top-ranked bands with the desired number.…”

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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Section: Introductionmentioning

confidence: 99%

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

Li,

Liu,

Hua

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…BS is to select a band subset that contains as much effective information as possible from the original band set. Compared with feature extraction methods [2,3], which utilize the complex feature transformation to obtain the reduced-dimensional HSIs, BS methods [4,5] can retain the physical information of the original HSI. In this sense, we focus mainly on BS methods.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, most ranking-based methods mainly consider the information of each band while ignoring the redundancy existing between the selected bands [12,14]. Moreover, most of the BS methods only consider the linear correlation between the bands or simply the nonlinear correlation based on the predefined kernel function and cannot analyze the inherent nonlinear correlation between the bands well [4,9]. In this context, some deep learning-based BS methods are proposed to consider the underlying nonlinear relationship between the bands.…”

Section: Introductionmentioning

confidence: 99%

EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection

Liu

Hua

et al. 2021

Remote Sensing

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Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing. However, most of the existing BS methods fail to fully consider the interaction between spectral bands and cannot comprehensively consider the representativeness and redundancy of the selected band subset. To solve these problems, we propose an unsupervised effective band attention reconstruction framework for band selection (EBARec-BS) in this article. The framework utilizes the EBARec network to learn the representativeness of each band to the original band set and measures the redundancy between the bands by calculating the distance of each unselected band to the selected band subset. Subsequently, by designing an adaptive weight to balance the influence of the representativeness metric and redundancy metric on the band evaluation, a final band scoring function is obtained to select a band subset that well represents the original hyperspectral image and has low redundancy. Experiments on three well-known hyperspectral data sets indicate that compared with the existing BS methods, the proposed EBARec-BS is robust to noise bands and can effectively select the band subset with higher classification accuracy and less redundant information.

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Crop-specific hyperspectral band selection method using limited ground-truth data

Dave

Trivedi

2022

International Journal of Remote Sensing

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Representativeness and Redundancy-Based Band Selection for Hyperspectral Image Classification`

Cited by 6 publications

References 45 publications

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection

Crop-specific hyperspectral band selection method using limited ground-truth data

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