Unsupervised  Hyperspectral Band Selection using Clustering and Single-Layer Neural Network

Habermann, Mateus; Frémont, Vincent; Shiguemori, Elcio Hideiti

doi:10.52638/rfpt.2018.419

Cited by 3 publications

(1 citation statement)

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“…Inspired by [41], the proposed method also performs clustering of the data entries. However, here, we adopted a partitional clustering instead of a hierarchical one, as illustrated in Figure 1a.…”

Section: Data Clusteringmentioning

confidence: 99%

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

Habermann¹,

Shiguemori²,

Frémont

2022

Remote Sensing

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A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes phenomenon. In order to attenuate such problems, one can resort to dimensionality reduction (DR). Thus, this paper proposes a new DR algorithm, which performs an unsupervised band selection technique following a clustering approach. More specifically, the data set was split into a predefined number of clusters, after which the bands were iteratively selected based on the parameters of a separating hyperplane, which provided the best separation in the feature space, in a one-versus-all scenario. Then, a fine-tuning of the initially selected bands took place based on the separability of clusters. A comparison with five other state-of-the-art frameworks shows that the proposed method achieved the best classification results in 60% of the experiments.

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Section: Data Clusteringmentioning

confidence: 99%

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

Habermann¹,

Shiguemori²,

Frémont

2022

Remote Sensing

Self Cite

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A Perceptron-Based Feature Selection Approach for Decision Tree Classification

Casaroti¹,

Centeno

Fuchs

2020

Bol. Ciênc. Geod.

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Hyper-graph regularized subspace clustering with skip connections for band selection of hyperspectral image

Zeng

Ning

et al. 2022

COMSIS J

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The Hughes phenomenon of Hyperspectral images (HSIs) with the hundreds of continuous narrow bands makes the computational cost of HSIs process ing high. Band selection is an effective way to solve such a problem and a lot of band selection methods have been proposed in recent years. In this paper, a novel hyper-graph regularized subspace clustering with skip connections (HRSC-SC) is proposed for band selection of hyperspectral image, which is a clustering-based band selection method. The networks combine subspace clustering into the convolutional auto-encoder by thinking of it as a self-expressive layer. To make full use of the historical feature maps obtained from the networks and tackle the problem of gradient vanishing caused by multiple nonlinear transformations, the symmetrical skip connections are added to the networks to pass image details from encoder to decoder. Furthermore, the hyper-graph regularization is presented to consider the manifold structure reflecting geometric information within data, which accurately describes the multivariate relationship between data points and makes the results of clustering more accurate so that select the most representative band subset. The proposed HRSC-SC band selection method is compared with the existing robust band selection algorithms on Indian Pines, Salinas-A, and Pavia University HSIs, showing that the results of the proposed method outperform the current state-of-the-art band selection methods. Especially, the overall accuracy of the clustering is the best on three real HSIs compared to other methods when the band selection number is 25, reaching 82.62%, 92.48%, and 96,5% respectively.

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Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network

Cited by 3 publications

References 0 publications

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification

A Perceptron-Based Feature Selection Approach for Decision Tree Classification

Hyper-graph regularized subspace clustering with skip connections for band selection of hyperspectral image

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