CHAPTER 12 The Classification Problem CLASSIFICATION A. Dermanis

Absolute Classification Prior determination of the spectral reflectance characteristics of all possible classes Creation of spectral libraries. Relative Classification Pixels are classified in the same class when their values in all bands are similar. Requires: Effective reduction of atmospheric effects (effective global monitoring of the atmosphere). Large number of well-distributed bands (hyperspectral or ultraspectral sensors) Requires: External data, collected by field work (at the same time epoch with satellite imagery). Supervised Classification: Ground data introduced before classification. Unsupervised Classification: Ground data introduced after classification. Restricting factor: Assessment of atmospheric parameters Restricting factor: Assessment of atmospheric parameters No Restriction: Atmospheric influence is the same, also for pixels with ground data information No Restriction: Atmospheric influence is the same, also for pixels with ground data information A. Dermanis

Absolute Clasification: Class centers determined from spectral library. Absolute Clasification: Class centers determined from spectral library. Relative Clasification: Unsupervised: Class centers determined from clustering algorithm. Relative Clasification: Unsupervised: Class centers determined from clustering algorithm. Relative Clasification: Supervised: Class centers determined from ground collected data (pixel samples for each class) Relative Clasification: Supervised: Class centers determined from ground collected data (pixel samples for each class) Classification by pixel position in spectral space A. Dermanis

Absolute definition of the classes not possible: Variation within each land cover type - No distinct class separation. Dependence on the particular application. Classification Problems Correction for atmospheric influence not completely possible: Global atmospheric monitoring: determines at atmospheric absorbance (T θ, T φ ) but not atmospheric diffusion due to scattering (E D, L P ) z(λ)  L S (λ) + A(λ) ρ(λ) + Β(λ) A(λ) = Τ φ (λ) [Τ φ (λ) Ε 0 (λ) cosω + Ε D (λ) 1π1π Β(λ) = L P (λ) A. Dermanis

Classification Problems Limited number of bands in multispectral sensors: Only a discrete version of spectral firm is viewed Presence of mixed pixelsVariation of the spectral signature within single class A. Dermanis