Share This Article:

A Simplified Approach for Interpreting Principal Component Images

Abstract Full-Text HTML XML Download Download as PDF (Size:3562KB) PP. 111-119
DOI: 10.4236/ars.2013.22015    6,043 Downloads   10,134 Views   Citations

ABSTRACT

Principal component transformation is a standard technique for multi-dimensional data analysis. The purpose of the present article is to elucidate the procedure for interpreting PC images. The discussion focuses on logically explaining how the negative/positive PC eigenvectors (loadings) in combination with strong reflection/absorption spectral behavior at different pixels affect the DN values in the output PC images. It is an explanatory article so that fuller potential of the PCT applications can be realized.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

R. Gupta, R. Tiwari, V. Saini and N. Srivastava, "A Simplified Approach for Interpreting Principal Component Images," Advances in Remote Sensing, Vol. 2 No. 2, 2013, pp. 111-119. doi: 10.4236/ars.2013.22015.

References

[1] R. A. Johnson and D. W. Wichern, “Applied Multivariate Statistical Analysis,” Prentice Hall, New Jersey, 2001.
[2] J. M. Lattin, J. D. Caroll and P. E. Green, “Analyzing Multivariate Data,” Brooks/Cole, Thomson Asia Pte. Ltd., Singapore, 2004.
[3] I. T. Jolliffe, “Principal Component Analysis,” SpringerVerlag, New York, 2002.
[4] P. M. Mather, “Computer Processing of Remotely-Sensed images,” John Wiley & Sons Ltd, West Sussex, 2004.
[5] C. Munyati, “Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on Kafue Flats, Zambia,” Geocarto International, Vol. 19, No. 3, 2002, pp. 11-22. doi:10.1080/10106040408542313
[6] T. Fung and E. LeDrew, “Application of Principal Components Analysis to Change Detection,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 53, No. 12, 1987, pp. 1649-1658.
[7] L. Eklundh and A. Singh, “A Comparative Analysis of Standardised and Unstandardised Principal Components Analysis in Remote Sensing,” International Journal of Remote Sensing, Vol. 14, No. 7, 1993, pp. 1359-1370. doi:10.1080/01431169308953962
[8] G. P. Quinn and M. J. Keough, “Experimental Design and Data Analysis for Biologists,” Cambridge University Press, Cambridge, 2002. doi:10.1017/CBO9780511806384
[9] EXELIS, “How to Figure out Principal Component Analysis Band Weightings,” 2000. http://www.exelisvis.com/Support/HelpArticlesDetail/TabId/219/ArtMID/900/ArticleID/2807/2807.aspx
[10] A. Singh and A. Harrison, “Standardised Principal Components,” International Journal of Remote Sensing, Vol. 6, No. 6, 1985, pp. 883-896. doi:10.1080/01431168508948511
[11] A. P. Crosta and J. M. Moore, “Enhancement of Landsat Themetic Mapper Imagery for Residual Soil Mapping in SW Minas Gerais State, Brazil: A Prospecting Case History in Greenstone Belt Terrain,” Proceedings of the Seventh Thematic Conference on Remote Sensing for Exploration Geology, Calgary, 2-6 October 1989, pp. 1173-1187.
[12] W. P. Loughlin, “Principal Component Analysis for Alteration Mapping,” Photogrammetric Enggineering and Remote Sensing, Vol. 57, No. 9, 1991, pp. 1163-1169.
[13] J. R. Ruiz-Armenta and R. M. Prol-Ledesma, “Techniques for Enhancing the Spectral Response of Hydrothermal Alteration Minerals in Thematic Mapper Images of Central Mexico,” International Journal of Remote Sensing, Vol. 19, No. 10, 1998, pp. 1981-2000. doi:10.1080/014311698215108
[14] M. H. Tangestani and F. Moore, “Porphyry Copper Alteration Mapping at the Meiduk Area, Iran,” International Journal of Remote Sensing, Vol. 23, No. 22, 2002, pp. 4815-4825. doi:10.1080/01431160110115564
[15] A. B. Pour and M. Hashim, “Spectral Transformation of ASTER Data and the Discrimination of Hydrothermal Alteration Minerals in a Semi-Arid Region, SE Iran,” International Journal of Physical Sciences, Vol. 6, No. 8, 2011, pp. 2037-2059.
[16] Y. Hirosawa, S. E. Marsh and D. H. Kliman, “Application of Standardized Principal Component Analysis to LandCover Characterization Using Multitemporal AVHRR Data,” Remote Sensing of Environment, Vol. 58, No. 3, pp. 267-281. doi:10.1016/S0034-4257(96)00068-5
[17] H. Holden and E. LeDrew, “Spectral Discrimination of Healthy and Non-Healthy Corals Based on Cluster Analysis, Principal Components Analysis, and Derivative Spectroscopy,” Remote Sensing of Environment, Vol. 65, No. 2, 1998, pp. 217-224. doi:10.1016/S0034-4257(98)00029-7
[18] D. Yuan, J. R. Lucas and D. E. Holland, “A Landsat MSS Time Series Model and Its Application in Geological Mapping,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 53, No. 1, 1998, pp. 39-53. doi:10.1016/S0924-2716(97)00027-0
[19] A. Li, A. Wang, S. Liang and W. Zhou, “Eco-Environmental Vulnerability Evaluation in Mountainous Region Using Remote Sensing and GIS—A Case Study in the Upper Reaches of Minjiang River, China,” Ecological Modelling, Vol. 192, No. 1-2, 2006, pp. 175-187. doi:10.1016/j.ecolmodel.2005.07.005
[20] K. H. M. Dewidar and O. E. Frihy, “Thematic Mapper Analysis to Identify Geomorphologic and Sediment Texture of El Tineh Plain, North-Western Coast of Sinai, Egypt,” International Journal of Remote Sensing, Vol. 24, No. 11, 2003, pp. 2377-2385. doi:10.1080/01431160110115807
[21] M. F. Kaiser, “Environmental Changes, Remote Sensing, and Infrastructure Development: The Case of Egypt’s East Port Said Harbor,” Applied Geology, Vol. 29, No. 2, 2009, pp. 280-288.
[22] J.-L. Ding, M.-C. Wu and T. Tiyip, “Study on Soil Salinization Information in Arid Region Using Remote Sensing Technique,” Agricultural Sciences in China, Vol. 10, No. 3, 2011, pp. 401-411.
[23] A. Sadiq and F. Howari, “Remote Sensing and Spectral Characteristics of Desert Sand from Qatar Peninsula, Arabian/Persian Gulf,” Remote Sensing, Vol. 1, No. 4, 2009, pp. 915-933. doi:10.3390/rs1040915
[24] N. Koutsias, G. Mallinis and M. Karteris, “A Forward/ Backward Principal Component Analysis of Landsat-7 ETMC Data to Enhance the Spectral Signal of Burnt Surfaces,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 64, No. 1, 2009, pp. 37-46. doi:10.1016/j.isprsjprs.2008.06.004

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.