2D<sup>2</sup>PCA-based hyperspectral image classification with utilization of spatial information

Aydemir, M. Said; Bilgin, Gökhan

doi:10.1109/whispers.2013.8080680

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In HSI, PCA has been successfully utilized for dimensionality reduction [ 24 , 27 , 28 ]. PCA’s ability to capture essential spectral variations and effectively reduce the dimensionality of HS data has led to its widespread adoption in this domain.…”

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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“…Here, eigenvectors are calculated to extract the main components of the image, which typically leads to high memory costs if both the numbers of samples and feature dimensions are high. To overcome these issues, [19], [39] proposed methods that depend on local and global cues, which can analyze contextual information. This study leveraged the contextual information to perform channel attention utilizing the matrix to identify the channels of the feature map that must be concentrated for finegrained classification.…”

Section: Utilization Of Feature Covariancementioning

confidence: 99%

Fine-Grained Classification via Hierarchical Feature Covariance Attention Module

et al. 2023

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Fine-Grained Visual Classification (FGVC) has consistently been challenging in various domains, such as aviation and animal breeds. It is mainly due to the FGVC's criteria that differ with a considerably small range or subtle pattern differences. In the deep convolutional neural network, the covariance between feature maps positively affects the selection of features to learn discriminative regions automatically. In this study, we propose a method for a finegrained classification model by inserting an attention module that uses covariance characteristics. Specifically, we introduce a feature map attention module (FCA) to extract the feature map between convolution blocks, constituting the existing classification model. The FCA module then applies the corresponding value of the covariance matrix to the channel to focus on the salient area. We demonstrate the need for fine-grained classification in a hierarchical manner by focusing on the diverse scale representation. Additionally, we implemented two ablation studies to show how each suggested strategy affects classification performance. Our experiments are conducted on three datasets, CUB-200-2011, Stanford Cars, and FGVC-Aircraft, primarily used for fine-grained classification tasks. Our method outperforms the state-of-the-art models by a margin of 0.4%, 1.1%, and 1.4%.

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“…In HSI, PCA has been successfully utilized for dimensionality reduction [19,22,23]. PCA's ability to capture essential spectral variations and effectively reduce the dimensionality of HS data has led to its widespread adoption in this domain.…”

Section: Pca For Hs Imagesmentioning

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

2D²PCA-based hyperspectral image classification with utilization of spatial information

Cited by 3 publications

References 14 publications

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

Fine-Grained Classification via Hierarchical Feature Covariance Attention Module

HyperVein: A Dataset for Human Vein Detection from Hyperspectral Images

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2D2PCA-based hyperspectral image classification with utilization of spatial information

Cited by 3 publications

References 14 publications

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

Fine-Grained Classification via Hierarchical Feature Covariance Attention Module

HyperVein: A Dataset for Human Vein Detection from Hyperspectral Images

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2D²PCA-based hyperspectral image classification with utilization of spatial information