A climatology of total ozone mapping spectrometer data using rotated principal component analysis

1999 ◽  
Vol 104 (D3) ◽  
pp. 3691-3709 ◽  
Author(s):  
Brian K. Eder ◽  
Sharon K. LeDuc ◽  
Joseph E. Sickles
Keyword(s):  
Principal Component Analysis ◽  
Total Ozone ◽  
Principal Component ◽  
Component Analysis ◽  
Total Ozone Mapping Spectrometer ◽  
Spectrometer Data ◽  
Rotated Principal Component Analysis

scholarly journals Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

2019 ◽  
Vol 45 (3) ◽  
pp. 522-535 ◽  
Author(s):  
Joseph D. Ortiz ◽  
Dulci M. Avouris ◽  
Stephen J. Schiller ◽  
Jeffrey C. Luvall ◽  
John D. Lekki ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Lake Erie ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Images ◽  
Western Basin ◽  
Derivative Spectroscopy ◽  
Rotated Principal Component Analysis

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

2011 ◽  
Vol 109 (1-2) ◽  
pp. 221-231 ◽  
Author(s):  
Goutami Chattopadhyay ◽  
Surajit Chattopadhyay ◽  
Parthasarathi Chakraborthy
Keyword(s):  
Principal Component Analysis ◽  
Ozone Concentration ◽  
Total Ozone ◽  
Principal Component ◽  
Component Analysis ◽  
Urban Regions

scholarly journals Entropy based determination of optimal principal components of Airborne Prism Experiment (APEX) imaging spectrometer data for improved land cover classification

2014 ◽  
Vol XL-8 ◽  
pp. 781-786 ◽  
Author(s):  
A. Kallepalli ◽  
A. Kumar ◽  
K. Khoshelham
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Land Cover ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Optimal Number ◽  
Computational Load ◽  
Spectrometer Data

Hyperspectral data finds applications in the domain of remote sensing. However, with the increase in amounts of information and advantages associated, come the "curse" of dimensionality and additional computational load. The question most often remains as to which subset of the data best represents the information in the imagery. The present work is an attempt to establish entropy, a statistical measure for quantifying uncertainty, as a formidable measure for determining the optimal number of principal components (PCs) for improved identification of land cover classes. Feature extraction from the Airborne Prism EXperiment (APEX) data was achieved utilizing Principal Component Analysis (PCA). However, determination of optimal number of PCs is vital as addition of computational load to the classification algorithm with no significant improvement in accuracy can be avoided. Considering the soft classification approach applied in this work, entropy results are to be analyzed. Comparison of these entropy measures with traditional accuracy assessment of the corresponding „hardened‟ outputs showed results in the affirmative of the objective. The present work concentrates on entropy being utilized for optimal feature extraction for pre-processing before further analysis, rather than the analysis of accuracy obtained from principal component analysis and possibilistic <i>c</i>-means classification. Results show that 7 PCs of the APEX dataset would be the optimal choice, as they show lower entropy and higher accuracy, along with better identification compared to other combinations while utilizing the APEX dataset.

Use of obliquely rotated principal component analysis to identify coherent structures

1996 ◽  
Vol 80 (1-2) ◽  
pp. 19-47 ◽  
Author(s):  
Donald K. Rinker ◽  
George S. Young
Keyword(s):  
Principal Component Analysis ◽  
Coherent Structures ◽  
Principal Component ◽  
Component Analysis ◽  
Rotated Principal Component Analysis
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