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Image Pre-Processing Continuation… Spectral Enhancement

Image Pre-Processing Continuation… Spectral Enhancement. ImagePre-Processing. Image Pre-Processing.

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Image Pre-Processing Continuation… Spectral Enhancement

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  1. Image Pre-Processing Continuation… Spectral Enhancement

  2. ImagePre-Processing Image Pre-Processing Consists of processes aimed at the geometric and radiometric correction, enhancement or standardization of imagery to improve our ability to interpret qualitatively and quantitatively image components. • Radiometric Enhancement: • Image Restoration • Atmospheric Correction • Contrast Enhancement • Solar Angle Adjustment • Conv. to Exo-Atmos. Reflectance • Spectral Enhancement: • Spectral Indices • PCA, IHS, Color Transforms • T-Cap, BGW • Spatial Enhancement: • Focal Analysis • Edge-Detection • High/Low Pass Filters • Resolution Merges • Statistical Filtering • Adaptive Filtering • Texture Filters • Geometric Correction • Polynomial Transformation • Ground Control Points • Reprojections

  3. Principal Component Analysis PCA Principal Components Analysis is a procedure for transforming a set of correlated variables into a new set of uncorrelated variables. This transformation is a rotation of the original axes to new orientations that are orthogonal to each other with little or no correlation between variables Where digital image processing is concerned, this procedure is predominantly exploratory in nature and is used to help in the extraction of features and to reduce dimensionality of data

  4. This scatterplot between two spectral bands implies a strong correlation. One band can be used to predict (to a certain level) the response of the other. • Principal Component Analysis PCA • Reduces these data into two orthogonal components. The first (CI) contains the common information between bands 1 and 2. The second (CII) contains residual, or independent, information. • Depending on the amount of covariance between bands 1 and 2, the second component may not contain a significant amount of information and can be eliminated. After Lillesand and Keifer, 1994 Source: http://umbc7.umbc.edu/~tbenja1/exer1.html

  5. Landsat Thematic Mapper image of Curlew Valley taken on July 4th, 1999 with a 4,3,2 (RGB) band combination.

  6. Eigenmatrix The Eigenmatrix contains the coefficients used to calculate each component for the input image. This matrix is a direct result of the covariance between each band. Eigenvalues The Eigenvalues show the amount of information contained within each component.

  7. Factor loadings = what type of component (i.e. visible, infrared) is it? Rkp: Factor loading akp:Eigenvector for band k and component p λp: Eigenvalue for the pth component Sk: Standard deviation for band k

  8. Landsat Thematic Mapper image of Curlew Valley taken on July 4th, 1999 converted to a principal component image with a 1,2,3 (RGB) PCA channel combination.

  9. PCA1 = (TM-B1)0.194 + (TM-B2)0.0413 + (TM-B3)‑0.332 + (TM-B4)0.196 + (TM-B5)‑0.078 + (TM-B6)0.640 + (TM-B7)‑0.628 Accounts for - 72.28% of variation PCA2 = (TM-B1)0.222 + (TM-B2)0.102 + (TM-B3)‑0.406 + (TM-B4)0.220 + (TM-B5)‑0.142 + (TM-B6)0.370 + (TM-B7)0.754 Accounts for – 22.07% of variation PCA3 = (TM-B1)0.348 + (TM-B2)0.092 + (TM-B3)‑0.495 + (TM-B4)0.312 + (TM-B5)‑0.209 + (TM-B6)-0.670 + (TM-B7)-0.184 Accounts for – 2.62% of variation

  10. PCA4 = (TM-B1)-0.232 + (TM-B2)0.962 + (TM-B3)0.045 + (TM-B4)0.071 + (TM-B5)0.105 + (TM-B6)-0.011 + (TM-B7)-0.034 Accounts for – 1.84% of variation PCA5 = (TM-B1)0.556 + (TM-B2)0.202 + (TM-B3)0.439 + (TM-B4)-0.289 + (TM-B5)-0.608 + (TM-B6)0.050 + (TM-B7)-0.009 Accounts for – 0.90% of variation PCA5 = (TM-B1)0.201 + (TM-B2)-0.053 + (TM-B3)0.533 + (TM-B4)0.801 + (TM-B5)0.170 + (TM-B6)0.017 + (TM-B7)0.023 Accounts for – 0.22% of variation

  11. PCA5 = (TM-B1)0.622 + (TM-B2)-0.094 + (TM-B3)-0.022 + (TM-B4)-0.289 + (TM-B5)0.720 + (TM-B6)-0.011 + (TM-B7)0.018 Accounts for – 0.07% of variation Components 1 – 3 account for 96.97% of the total variation While the remaining components only account for a combined 3.03% of the total variation, there may be spatial information available.

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