<![CDATA[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]]>
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