RETRACTED ARTICLE: Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification

Reshma, R.; Sowmya, V.

doi:10.1007/s00521-017-3145-y

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…For HSI classification, Ramamurthy et al 66 employed a CNN-based classifier with an auto-encoder-based dimensionality reduction technique. Singular Value Decomposition (SVD) followed by the combination of QR decomposition and Inter-Band Block Correlation Coefficient (IBBC) was presented by Reshma et al 67 to improve the spatial features. For dimensionality reduction, Li et al 68 developed another spatial-spectral-based neighbour graph.…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Reddy

Harikiran

2022

J. Spectral Imaging

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Hyperspectral imaging is used in a wide range of applications. When used in remote sensing, satellites and aircraft are employed to collect the images, which are used in agriculture, environmental monitoring, urban planning and defence. The exact classification of ground features in the images is a significant research issue and is currently receiving greater attention. Moreover, these images have a large spectral dimensionality, which adds computational complexity and affects classification precision. To handle these issues, dimensionality reduction is an essential step that improves the performance of classifiers. In the classification process, several strategies have produced good classification results. Of these, machine learning techniques are the most powerful approaches. As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. Moreover, this paper shows the effectiveness of all these techniques for hyperspectral image classification and dimensionality reduction. Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.

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Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Reddy

Harikiran

2022

J. Spectral Imaging

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“…To improve the detection performance, another key contribution of this paper is that the algorithm uses the standard deviation to process the parts of DOCT coefficient matrix. Finally, the LIBSVM is employed for classification [13, 14]. This paper improves the parameter selection method of the LIBSVM classifier in order to improve the performance of the LIBSVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Image splicing detection based on Markov features in discrete octonion cosine transform domain

et al. 2018

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To improve the poor robustness and low accuracy of the existing algorithms of image splicing detection, a novel passive image forgery detection method is proposed in this study, which is based on DOCT (discrete octonion cosine transform) and Markov. By introducing the octonion and DOCT, the colour information of six image channels (the RGB model and the HSI model) can be exhaustively extracted, which enhances the robustness of the algorithm. On the issue of improving the detection accuracy, the standard deviation is used to characterise the relationship of the colour information between the parts of DOCT coefficient matrix, and the K‐fold cross‐validation is introduced to improve the identification performance of the classifier. The steps of the algorithm are as follows: Firstly, the 8 × 8 block DOCT transform is used to the original image to obtain parts of block DOCT coefficient. Secondly, the standard deviation is used to process the corresponding parts of all blocks of the image. Finally, the Markov feature vector of the DOCT coefficient is extracted and feds to the LIBSVM (a library for support vector machines). When using LIBSVM for classification, K‐fold cross‐validation is executed to select the best parameter pairs. The experiment results demonstrate that the algorithm is superior to the other state‐of‐the‐art splicing detection methods.

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Hyperspectral Image: Fundamentals and Advances

Sowmya¹,

Soman²,

Hassaballah³

2018

Studies in Computational Intelligence

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RETRACTED ARTICLE: Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification

Cited by 12 publications

References 19 publications

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Image splicing detection based on Markov features in discrete octonion cosine transform domain

Hyperspectral Image: Fundamentals and Advances

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