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Principal Component Analysis Transform for Change Detection to Satellite Imagery | ||
| Journal of College of Education | ||
| Article 1, Volume 0, Issue 1, 2017, Pages 212-223 | ||
| Authors | ||
| S. J. Kadim; H. I. Ali | ||
| Abstract | ||
| Detecting the change in regions of multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible .Change detection from PCA shows a good result and fast method. In this paper, (PCA) method is used and its experimental results demonstrate the interest of the approach as another method. | ||
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