Improving urban classification through fuzzy supervised classification and spectral mixture analysis

In this study, a fuzzy-spectral mixture analysis (fuzzy-SMA) model was developed to achieve land use/land cover fractions in urban areas with a moderate resolution remote sensing image. Differed from traditional fuzzy classification methods, in our fuzzy-SMA model, two compulsory statistical measurements (i.e. fuzzy mean and fuzzy covariance) were derived from training samples through spectral mixture analysis (SMA), and then subsequently applied in the fuzzy supervised classification. Classification performances were evaluated between the 'estimated' landscape class fractions from our method and the 'actual' fractions generated from IKONOS data through manual interpretation with heads-up digitizing option. Among all the sub-pixel classification methods, fuzzy-SMA performed the best with the smallest total_MAE (MAE, mean absolute error) (0.18) and the largest Kappa (77.33%). The classification results indicate that a combination of SMA and fuzzy logic theory is capable of identifying urban landscapes at sub-pixel level.