Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising

Luo, Guangchun; Chen, Guangyi; Tian, Ling; Qin, Ke; Qian, Shen‐En

doi:10.1080/07038992.2016.1160772

Cited by 154 publications

(66 citation statements)

References 27 publications

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“…MNF is a linear transform consisting of two different steps: (1) computation of the covariance matrix to decorrelate and rescale the noise in the data; and (2) the performance of a standard PCA transform of the decorrelated and rescaled noise data. The goal of the MNF transform is to select components such they maximize the Signal-to-Noise Ratio (SNR), which compares the level of the signal to the level of the background noise rather than the information content [13]. The fact of ordering the components according to the amount of information results in a more reliable identification and elimination of noisy components, and allows for the preservation of components that contain useful information [9,13].…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

“…It is a mathematical orthogonal transformation that changes a set of observations of possibly correlated variables into a set of uncorrelated variables called principal components [31]. PCA retains most of the information of the original data in a low-dimensional space [13]. Conventional PCA faces three main challenges: (1) obtaining a covariance matrix in an extremely large spatial dimension; (2) dealing with the high computational cost required for the analysis of a large dataset; and (3) retaining locally structured elements that appear in a small number of bands for improved discriminant ability when feature bands are globally extracted as principal components [1].…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

“…Besides that, a lack of a comparative study on traditional techniques used in HSI dimensionality reduction has been observed as well as an accurate approach for component selection in the transformed space. There exist studies comparing PCA, MNF, and ICA with new methodologies [9,[13][14][15], as well as other studies that analyze their behavior or improve these techniques [1,2,5,11,[16][17][18], without making a comparison with the existing methods. Additionally, previous studies [8,19,20] have used a small number of components to obtain adequate classification maps.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Component Selection Strategies in Hyperspectral Imagery

Ibarrola-Ulzurrun

Marcello

Gonzalo-Martín

2017

Entropy

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Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the 'Hughes' phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.

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Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Component Selection Strategies in Hyperspectral Imagery

Ibarrola-Ulzurrun

Marcello

Gonzalo-Martín

2017

Entropy

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“…where λ 1 is the regularization parameter that is used to weigh the impact of the TV norm on the minimum of the objective function. In this paper, the reconstruction algorithm using (7) as the optimization function is denoted as BCS_TV, in which the image is sampled by the BCS method and reconstructed using the TV norm. The way to solve the optimization problem is named TVAL3 [36].…”

Section: Optimization Problem Using Tv Norm Differentmentioning

confidence: 99%

Compressed sensing reconstruction of hyperspectral images jointly using spatial smoothing feature and spectral correlation

Wang

Feng

Wang

2017

IEEJ Transactions Elec Engng

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A compressed sensing (CS) reconstruction algorithm of hyperspectral images jointly using spatial and spectral characteristics is considered. Specifically, in the sampling process, each band image is sampled by the block CS method independently. In the reconstruction process, how to utilize the spatial smoothing feature of each band image and spectral correlation between different band images to formulate the joint optimization problem is the focus of this paper. The total variation (TV) norm and multihypothesis prediction are introduced to express the spatial smoothing feature and the spectral correlation, respectively. Thus, the TV norm and the prediction residual are used as the regularization items in the reconstruction optimization problem. The resulting ill‐posed problem is solved by the augmented Lagrange multiplier method and alternating direction method in an iterative way, and the implementation process of the reconstruction algorithm is presented. Experimental results on four hyperspectral datasets reveal that the proposed algorithm significantly outperforms alternative strategies in terms of peak signal‐to‐noise ratio as well as visual quality. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Just like PCA, MNF also transforms original data into a new dimensional space whose eigenvectors are orthogonal to other eigenvectors. MNF has been used by many hyperspectral and multispectral studies to reduce the data dimensions [17,43,[45][46][47]. ENVI 5.2 was used to perform PCA and MNF transformations.…”

Section: Methodsmentioning

confidence: 99%

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Nimbalkar

Jarocińska

Zagajewski

2018

Journal of Spectroscopy

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Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth's object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.

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Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising

Cited by 154 publications

References 27 publications

Assessment of Component Selection Strategies in Hyperspectral Imagery

Assessment of Component Selection Strategies in Hyperspectral Imagery

Compressed sensing reconstruction of hyperspectral images jointly using spatial smoothing feature and spectral correlation

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

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