Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images

Sun, He; Zhang, Lei; Ren, Jinchang; Huang, Hua

doi:10.1016/j.patcog.2022.108788

Cited by 22 publications

(2 citation statements)

References 35 publications

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“…In general, two types of techniques were developed in this regard, in a supervised [4] or unsupervised learning manner. These are band-selection techniques [5,6], which select the most informative subset of the bands, and feature extraction techniques [7] which transform the data to a lower dimension. In [8], a feature extraction method called improving distribution analysis (IDA) is proposed which aims to simplify the data and the computational complexity of the HSI classification model.…”

Section: Introductionmentioning

confidence: 99%

Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images

Haidar,

Oramas

2023

Remote Sensing

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Hyperspectral remote sensing images, with their amalgamation of spectral richness and geometric precision, encapsulate intricate, non-linear information that poses significant challenges to traditional machine learning methodologies. Deep learning techniques, recognised for their superior representation learning capabilities, exhibit enhanced proficiency in managing such intricate data. In this study, we introduce a novel approach in hyperspectral image analysis focusing on multi-label, patch-level classification, as opposed to applications in the literature concentrating predominantly on single-label, pixel-level classification for hyperspectral remote sensing images. The proposed model comprises a two-component deep learning network and employs patches of hyperspectral remote sensing scenes with reduced spatial dimensions yet with a complete spectral depth derived from the original scene. Additionally, this work explores three distinct training schemes for our network: Iterative, Joint, and Cascade. Empirical evidence suggests the Joint approach as the optimal strategy, but it requires an extensive search to ascertain the optimal weight combination of the loss constituents. The Iterative scheme facilitates feature sharing between the network components from the early phases of training and demonstrates superior performance with complex, multi-labelled data. Subsequent analysis reveals that models with varying architectures, when trained on patches derived and annotated per our proposed single-label sampling procedure, exhibit commendable performance.

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Section: Introductionmentioning

confidence: 99%

Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images

Haidar,

Oramas

2023

Remote Sensing

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“…Feature selection, also known as band selection, refers to selecting representative bands on the original dataset as the output bands using a particular method [18]. Since band selection can preserve the original spectral and physical information without destroying the hyperspectral data, scholars have researched it extensively [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Improved SR-SSIM Band Selection Method Based on Band Subspace Partition

Ting-xing

Gao

et al. 2023

Remote Sensing

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Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection methods. However, existing structural similarity methods calculate all the structural similarity between bands, which leads to excessively long runtimes for these methods. Aiming to address this problem, this paper proposes a band subspace partition method and combines it with the SR-SSIM band selection method to obtain an improved band selection method: E-SR-SSIM. E-SR-SSIM consists of two parts: band subspace partition and band subspace band selection. In the first part, the hyperspectral dataset is divided into subdatasets corresponding to a number of subspaces. In the second part, a modified SR-SSIM method is used for all subdatasets to select the most representative band in each subdataset. The Indian Pines, Salinas Kennedy Space Center and Wuhan unmanned aerial vehicle-borne hyperspectral image LongKou public datasets are used to implement the experiment. The experiment uses random forest as the supervised classifier: 10% of each category sample is randomly selected as training data, and the remaining 90% is used as test data. The evaluation indicators selected in the experiment are overall accuracy, average accuracy, kappa and recall. The experimental results show that E-SR-SSIM can effectively reduce the runtime while ensuring classification compared with SR-SSIM, and quantitative proof that the band subspace partition reduces the calculated amount of structural similarity is obtained through a mathematical analysis. The improved band subspace partition method could partition a dataset more reasonably than the original band subspace partition method.

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GSA-UBS: A Novel Medical Hyperspectral Band Selection Based on Gravitational Search Algorithm

Zhang,

Yang,

Zhang

et al. 2024

Lecture Notes in Computer Science

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Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images

Cited by 22 publications

References 35 publications

Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images

Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images

Improved SR-SSIM Band Selection Method Based on Band Subspace Partition

GSA-UBS: A Novel Medical Hyperspectral Band Selection Based on Gravitational Search Algorithm

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