Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification

Mishu, Sadia Zaman; Ahmed, Boshir; Hossain, Md. Ali; Uddin, Md. Palash

doi:10.1080/01431161.2020.1763502

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…A larger H enables capturing broader spatial interactions but creates increased computational complexity. FPCA has previously been successfully applied in HSI for efficient dimensionality reduction and feature extraction [ 28 , 29 , 30 ].…”

Section: Enhancement Of Vein Detection Methodologymentioning

confidence: 99%

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

Ndu,

Sheikh-Akbari,

Deng

et al. 2024

Sensors

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HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

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Section: Enhancement Of Vein Detection Methodologymentioning

confidence: 99%

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

Ndu,

Sheikh-Akbari,

Deng

et al. 2024

Sensors

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“…To further improve classification results of HSIs, the feature-selection step is usually employed. It is a popular practice to apply PCA and its variants [16][17][18][19][20][21][22][23][24] to perform such data transformation. For instance, the extended morphological profiles (EMPs) are built on the first principal component of HSI to eliminate the effects of spectral variability [18].…”

Section: Introductionmentioning

confidence: 99%

“…Zabalza et al [19] proposed the folded-PCA (FPCA) to handle with the high computational cost and large memory requirement for the usage of PCA in HSI data reduction. Considering the superior properties of FPCA, several FPCA-based models are newly designed for HSI classification [20][21]. In special, Uddin et al [21] proposed a novel method so-called spectrally-segmented-FPCA (SSFPCA), in which they apply FPCA on the HSI datasets mixed with highly correlated and spectrally separated segments instead of directly applying the FPCA on the entire data, to obtain better classification performances.…”

Section: Introductionmentioning

confidence: 99%

Kernel Low-Rank Entropic Component Analysis for Hyperspectral Image Classification

Bai

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Principal component analysis (PCA) and its variations are still the primary tool for feature extraction (FE) in remote sensing community. This is unfortunate, as there has been strong argument against using PCA for this purpose due to its inherent linear properties and uninformative principal components. Therefore, several critical issues still should be considered in hyperspectral image classification when using PCA, among which: 1) the large number of spectral channels and small number of training samples; 2) the nonlinearities of hyperspectral data; 3) the small-sample issue. In order to alleviate these problems, this paper employs a new information-theoretic FE method so-called kernel entropic component analysis (KECA), which can not only extract more nonlinear information, but also be adapt to limited-sample environment. A theorem of the pivoted Cholesky decomposition is also introduced to improve the efficiency of the KECA. The optimized version can more rapidly implement spectral-spatial features extraction, particularly for large-scale HSIs, while effectively maintaining the clustering performances of KECA. Experiments implemented on several real HSIs verify the effectiveness of the new method armed with a support vector machine (SVM) classifier, in comparison with other PCA-based and state-of-the-art HSI classification algorithms. The code will be also made publicly available.

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mentioning

confidence: 99%

HyperVein: A Dataset for Human Vein Detection from Hyperspectral Images

Ndu,

Sheikh-Akbari,

Deng

et al. 2023

Preprint

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Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with The Support Vector Machine binary classification were determined using the Training dataset. The performance of the three dimensionality reduction based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

show abstract

Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification

Cited by 13 publications

References 39 publications

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

Kernel Low-Rank Entropic Component Analysis for Hyperspectral Image Classification

HyperVein: A Dataset for Human Vein Detection from Hyperspectral Images

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