scholarly journals Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

2017 ◽  
Vol 05 (07) ◽  
pp. 39-48
Author(s):  
Manyun Lin ◽  
Xiangang Zhao ◽  
Cunqun Fan ◽  
Lizi Xie ◽  
Lan Wei ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Satellite Data ◽  
Principal Component ◽  
Component Analysis ◽  
Data Processing Software ◽  
Software Classification ◽  
Processing Software ◽  
Meteorological Satellite

scholarly journals Temporal Downscaling of TRMM Rain-Rate Images Using Principal Component Analysis during Heavy Tropical Thunderstorm Seasons

2015 ◽  
Vol 16 (5) ◽  
pp. 2264-2275 ◽  
Author(s):  
M. Rizaludin Mahmud ◽  
Hiroshi Matsuyama ◽  
Tetsuro Hosaka ◽  
Shinya Numata ◽  
Mazlan Hashim
Keyword(s):  
Principal Component Analysis ◽  
Satellite Data ◽  
Rain Rate ◽  
Daily Rainfall ◽  
Principal Component ◽  
Component Analysis ◽  
Eastern Coast ◽  
Rain Gauges ◽  
Hourly Rainfall ◽  
Rainfall Estimates

Abstract This paper examines the utility of principal component analysis (PCA) in obtaining accurate daily rainfall estimates from 3-hourly Tropical Rainfall Measuring Mission (TRMM) satellite data during heavy precipitation in a humid tropical environment. A large bias during heavy thunderstorms in humid tropical catchments is indicated by the TRMM satellite and is of profound concern because it is a conspicuous constraint for practical hydrology applications and requires proper treatment, particularly in areas with sparse rain gauges. The common procedure of calculating daily rainfall estimates by direct accumulation (DA) of a series of 3-hourly rainfall estimates caused a large bias because of temporal uncertainties, upscaling effects, and different mechanisms. In this study, PCA was used to transform correlated 3-hourly rain-rate images into a minimum effective principal component and to compute the corresponding rain-rate proportion based on correlation strength. This study was conducted on 91 rainy days of various intensity, acquired from three different years, during the wettest season on the eastern coast of peninsular Malaysia. Results showed that PCA reduced the bias and daily root-mean-square error by an average of 62% and 22%, respectively, compared with the DA approach. The PCA transformation was able to produce more precise daily rainfall estimates compared to the DA approach without the use of any rain gauge references. However, the performance was varied by the threshold selection and rainfall intensity. The results of this study indicate that PCA can be a useful tool in effective temporal downscaling of TRMM satellite data during heavy thunderstorm seasons in areas where rain gauges are sparse and satellite data are pivotal as a secondary source of rainfall 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

Reflectance interpretation of ALOS AVNIR-2 satellite data for ocean monitoring by means of principal component analysis

2015 ◽  
Author(s):  
Saumi Syahreza ◽  
Beh Boon Chun ◽  
Mohd Zubir Mat Jafri ◽  
Lim Hwee San ◽  
Khiruddin Abdullah
Keyword(s):  
Principal Component Analysis ◽  
Satellite Data ◽  
Principal Component ◽  
Component Analysis ◽  
Ocean Monitoring

scholarly journals Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors

2008 ◽  
Vol 8 (7) ◽  
pp. 1310-1316 ◽  
Author(s):  
P. Beatriz Garcia-Allende ◽  
Olga M. Conde ◽  
JesÚs Mirapeix ◽  
Ana M. Cubillas ◽  
JosÉ M. Lopez-Higuera
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Processing Method ◽  
Spectral Angle Mapper ◽  
Data Processing Method ◽  
Spectroscopic Sensors ◽  
Spectral Angle

Object Classification via PCANet and Color Constancy Model

2014 ◽  
Vol 635-637 ◽  
pp. 997-1000 ◽  
Author(s):  
De Kun Hu ◽  
Li Zhang ◽  
Wei Dong Zhao ◽  
Tao Yan
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Functional Properties ◽  
Principle Component Analysis ◽  
Color Constancy ◽  
Principal Component ◽  
Object Classification ◽  
Component Analysis ◽  
Color Images ◽  
Principle Component

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images.

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