scholarly journals Hyperspectral data processing algorithm combining principal component analysis and K nearest neighbours

2008 ◽  
Author(s):  
P. Beatriz Garcia-Allende ◽  
Olga M. Conde ◽  
Marta Amado ◽  
Antonio Quintela ◽  
Jose M. Lopez-Higuera
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Processing Algorithm ◽  
Data Processing Algorithm ◽  
Nearest Neighbours

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

scholarly journals A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma

The Analyst ◽  
2019 ◽  
Vol 144 (7) ◽  
pp. 2312-2319 ◽  
Author(s):  
Camilo L. M. Morais ◽  
Pierre L. Martin-Hirsch ◽  
Francis L. Martin
Keyword(s):  
Ovarian Cancer ◽  
Principal Component Analysis ◽  
Blood Plasma ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Exploratory Analysis ◽  
Hyperspectral Images ◽  
Analysis Approach

Three-dimensional principal component analysis (3D-PCA) for exploratory analysis of hyperspectral images.

scholarly journals Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Hamidullah Binol
Keyword(s):  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Ensemble Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Feature Representation ◽  
Kernel Principal Component Analysis ◽  
Kernel Pca ◽  
Multiple Kernel

Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representation for HSI. The specified technique employs several KPCAs simultaneously to obtain better feature points from each applied KPCA which includes different candidate kernels. Nevertheless, A-KPCA neglects the influence of subkernels employing an unweighted combination. Furthermore, if there is at least one weak kernel in the set of kernels, the classification performance may be reduced significantly. To address these problems, in this paper we propose an Ensemble Learning (EL) based multiple kernel PCA (M-KPCA) strategy. M-KPCA constructs a weighted combination of kernels with high discriminative ability from a predetermined set of base kernels and then extracts features in an unsupervised fashion. The experiments on two different AVIRIS hyperspectral data sets show that the proposed algorithm can achieve a satisfactory feature extraction performance on real data.

Raman imaging and principal component analysis-based data processing on uranium oxide ceramics

2017 ◽  
Vol 129 ◽  
pp. 260-269 ◽  
Author(s):  
O.A. Maslova ◽  
G. Guimbretière ◽  
M.R. Ammar ◽  
L. Desgranges ◽  
C. Jégou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Uranium Oxide ◽  
Principal Component ◽  
Component Analysis ◽  
Raman Imaging ◽  
Oxide Ceramics
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