Jalal Ibrahim Faraj (1), Ayad Jumaah Kadhim (2)
General Background: Dimensionality reduction is a critical technique in image processing, especially for multispectral satellite imagery where data redundancy and computational complexity are prevalent challenges. Specific Background: Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are two widely adopted methods for reducing dimensionality while preserving essential image information. Knowledge Gap: Despite their extensive usage, comparative assessments of their performance in multispectral image reconstruction, particularly in geospatial contexts, remain limited. Aims: This study aims to evaluate and compare the effectiveness of PCA and ICA in processing Landsat multispectral images of the Bighorn Basin by assessing image reconstruction fidelity. Results: The findings reveal that PCA outperforms ICA in reconstruction quality, achieving higher Peak Signal-to-Noise Ratio (PSNR) values (up to 27.78 dB) and lower Root Mean Square Error (RMSE), whereas ICA, though proficient in extracting statistically independent features, demonstrated lower fidelity (PSNR = 17.63 dB). Novelty: The work offers a rigorous, side-by-side quantitative analysis of PCA and ICA applied to real-world satellite data, highlighting variance behavior and reconstruction trade-offs. Implications: These insights inform the selection of dimensionality reduction techniques in remote sensing tasks—PCA for optimal reconstruction and noise elimination, and ICA for feature extraction based on statistical independence.Highlights:
PCA provides superior image reconstruction accuracy with higher PSNR and lower RMSE.
ICA excels in isolating statistically independent features for advanced analysis.
PCA components show faster variance decay, making them efficient for compression.
Keywords: Dimensionality Reduction, Satellite Imagery, Principal Component Analysis, Independent Component Analysis, Image Reconstruction
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. Pearson, 2018.
A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. Hoboken, NJ, USA: John Wiley & Sons, 2001.
I. T. Jolliffe and J. Cadima, “Principal Component Analysis: A Review and Recent Developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, p. 20150202, 2016, doi: 10.1098/rsta.2015.0202.
W. K. Pratt, Digital Image Processing: PIKS Scientific Inside, 4th ed. Hoboken, NJ, USA: Wiley, 2007.
J. R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed. Pearson, 2016.
E. W. Averill, Elements of Statistics. New York, NY, USA: John Wiley & Sons, 1972.
S. Mohd and S. Kanak, “Analysis of Missing Value Estimation Algorithms for Data Farming,” An International Journal of Engineering Sciences, vol. 4, pp. 496–504, 2011. [Online]. Available: http://www.ivsl.org
E. W. Weisstein, “Covariance,” MathWorld – A Wolfram Web Resource. [Online]. Available: http://mathworld.wolfram.com/Covariance.html
N. J. Higham, F. Tisseur, and B. Cheng, Numerical Linear Algebra. Manchester, UK: University of Manchester, 2001. [Online]. Available: http://www.maths.manchester.ac.uk
I. Smith, “A Tutorial on Principal Component Analysis,” Cornell University, 2005. [Online]. Available: https://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2008.
A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ, USA: Prentice Hall, 1989.
J. R. Eastman, Introduction to Remote Sensing and Image Processing. Worcester, MA, USA: IDRISI Guide to GIS and Image Processing, Vol. 1, 2001.
S. Deepali, S. Pallavi, and M. A. Joshi, “PCA-Based Image Fusion for Multispectral Palm Enhancement,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 2, pp. 7501–7508, 2014.
Z. Jing, W. Lei, and T. Longzheng, “Feature Reduction and Texture Classification in MRI-Texture Analysis of Multiple Sclerosis,” in Proc. IEEE/ICME Int. Conf. Complex Medical Engineering, 2007, pp. 752–757. [Online]. Available: http://www.ivsl.org
A. Hyvarinen and E. Oja, “Independent Component Analysis: Algorithms and Applications,” Neural Networks, vol. 13, no. 4–5, pp. 411–430, 2000, doi: 10.1016/S0893-6080(00)00026-5.
P. Comon, “Independent Component Analysis—A New Concept,” Signal Processing, vol. 36, no. 3, pp. 287–314, 1994, doi: 10.1016/0165-1684(94)90029-9.