A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improving the classification of Landsat data using standardized principal components analysis
The standardized principal component analysis (SPCA) is the method that uses the correlation matrix instead of the covariance matrix, and then eigenvalues and eigenvectors from SPCA apply to image processing procedures. When each principal components column is compared between the eigenvector matrices from two different time images, the sign of principal components indicates the possibility in land-use/land-cover changes. The Landsat ETM+2000 was obtained in Suwon with 30 meter ground spatial resolution. The principal component 2 explains the difference between urban and vegetation areas through all bands. Two bands, band 2 and band 7, show the change in sign, meaning that urban areas and vegetation areas might distinctively show the characteristics if band 2 and band 7 are used in classifications. The classification is applied to composite images, which are the PC2, and 2, and band 7 composite image and the band 1, band 3, and band 5 composite image from the signature separability analysis. The principal component analysis is a useful statistical measurement for selecting a band combination including the principal component images. The eigenvector matrix can provide the band determination from the sign changes. The SPCA also gives critical information on determining the appropriate principal component, and the classification results from the accuracy assessment matrix described the large improvement on agricultural lands and bare lands.
Improving the classification of Landsat data using standardized principal components analysis
The standardized principal component analysis (SPCA) is the method that uses the correlation matrix instead of the covariance matrix, and then eigenvalues and eigenvectors from SPCA apply to image processing procedures. When each principal components column is compared between the eigenvector matrices from two different time images, the sign of principal components indicates the possibility in land-use/land-cover changes. The Landsat ETM+2000 was obtained in Suwon with 30 meter ground spatial resolution. The principal component 2 explains the difference between urban and vegetation areas through all bands. Two bands, band 2 and band 7, show the change in sign, meaning that urban areas and vegetation areas might distinctively show the characteristics if band 2 and band 7 are used in classifications. The classification is applied to composite images, which are the PC2, and 2, and band 7 composite image and the band 1, band 3, and band 5 composite image from the signature separability analysis. The principal component analysis is a useful statistical measurement for selecting a band combination including the principal component images. The eigenvector matrix can provide the band determination from the sign changes. The SPCA also gives critical information on determining the appropriate principal component, and the classification results from the accuracy assessment matrix described the large improvement on agricultural lands and bare lands.
Improving the classification of Landsat data using standardized principal components analysis
KSCE J Civ Eng
Chang, Hoon (author) / Yoon, Wan Seok (author)
KSCE Journal of Civil Engineering ; 7 ; 469-474
2003-07-01
6 pages
Article (Journal)
Electronic Resource
English
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