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HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS
Hyperspectral Images Noise Reduction Nonlocal Similarity Spectral Spatial Information Principal Component Analysis
2018/5/14
Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classi...
Potential reasons for ionospheric anomalies detected by nonlinear principal component analysis just before the China Wenchuan earthquake, and their relationship to source conditions
Nonlinear Principal Component Analysis (NLPCA) Principal Component Analysis (PCA) Total Electron Content (TEC),
2015/8/27
Nonlinear principal component analysis (NLPCA) was performed to examine the total electron content (TEC) anomalies for the China Wenchuan earthquake of May 12, 2008 (= 7.9). This was applied to global...
Ionospheric perturbations associated with two huge earthquakes in Japan, using principal component analysis for multiple subionospheric VLF/LF propagation paths
Ionospheric perturbations Earthquakes Subionospheric VLF/LF propagation
2015/8/24
The presence of ionospheric perturbations in possible association with two huge earthquakes (Noto-hanto peninsula and Niigata-chuetu-oki earthquakes) in 2007 was studied on the basis of a conventional...
Sparse Principal Component Analysis
Arrays Gene expression Lasso/elastic net Multivariate analysis Singular value decomposition Thresholding
2015/8/21
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However,PCA suffers from the fact that each principal component is a linear combination of all the or...
MULTIVARIATE MATHEMATICAL MORPHOLOGY BASED ON PRINCIPAL COMPONENT ANALYSIS: INITIAL RESULTS IN BUILDING EXTRACTION
Multichannel image processing colour morphology vector ordering principal component analysis urban analysis
2015/7/30
Today, colour or multichannel satellite and aerial images are increasingly becoming available due to the commercial availability of
multispectral digital sensors and pansharpening function of the co...
Robust Principal Component Analysis?
Principal components robustness vis-a-vis outliers nuclear-norm minimization `1-norm minimization duality low-rank matrices sparsity video surveillance
2015/6/17
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove...
Spitzer spectral line mapping of supernova remnants. I. Basic data and principal component analysis
Molecules Abundances
2014/12/23
We report the results of spectroscopic mapping observations carried out toward small (1' × 1') regions within the supernova remnants W44, W28, IC 443, and 3C 391 using the Infrared Spectrograph (IRS) ...
The evaluation of ground water pollution in alluvial and crystalline aquifer by Principal Component Analysis
Principal Component Analysis–Groundwater
2014/11/26
In order to evaluate the groundwater pollution, the application of statistical principal
components analysis (PCA) was used as one useful tool. PCA was based on the physical–
chemical data of groun...
Studies on heavy metals in industrial effluent, river and groundwater of Savar industrial area, Bangladesh by Principal Component Analysis
Industrial effluent Wastewater Heavy metals Principal component analysis Statistical analysis
2014/11/12
A total number of twenty water samples of which seven groundwater, six river water and seven effluent samples were collected from Savar industrial area in Bangladesh for heavy metals analysis using IC...
Approximation Bounds for Sparse Principal Component Analysis
Sparse PCA convex relaxation semidefinite programming approximation bounds detection
2012/5/9
We produce approximation bounds on a semidefinite programming relaxation for sparse principal component analysis. These bounds control approximation ratios for tractable statistics in hypothesis testi...
Groundwater Hydrograph Patterns in North China Plain during 1982-1986 Interpreted Using Principal Component Analysis
Groundwater Depth, North China Plain, Principal Component Analysis (PCA)
2011/12/16
The groundwater table depths from 1982 to 1986 of 58 unconfined wells in North China Plain(NCP) were analyzed using principal component analysis method. Results showed there were mainly three hydrogra...
Reionization constraints using Principal Component Analysis
dark ages reionization first stars intergalactic medium cosmology
2010/11/12
Using a semi-analytical model developed by Choudhury & Ferrara (2005) we study the observational
constraints on reionization via a principal component analysis (PCA). Assuming
that reionization at z...
Seasonal and spatial variations analysis of pollution status of Ondo coastal environment Nigeria using principal component analysis
PCA sediments seasonal and spatial variation metals coastal area
2017/8/9
The variation in environmental quality of Ondo coast between seasons and sites were evaluated using principal component analysis (PCA). Seventeen metals were analysed in water and sediments with five ...
Principal component analysis of geoelectrical signals measured in the seismically active area of Basilicata Region (southern Italy)
principal component analysis geoelectrical signals the seismically active area Basilicata Region
2009/12/14
Geoelectrical fluctuations are the end product of several geophysical phenomena. In particular geoelectrical signals measured in seismically active areas can be attributed to stress and strain changes...
Non-linear complex principal component analysis of nearshore bathymetry
Non-linear complex principal component analysis nearshore bathymetry
2009/11/11
Complex principal component analysis (CPCA) is a useful linear method for dimensionality reduction of data sets characterized by propagating patterns, where the CPCA modes are linear functions of the ...