Systematic Study of the Urban Postconflict Change Classification Performance Using Spectral and Structural Features in a Support Vector Machine

The state of built-up features after their destruction, as well as the process of their rehabilitation, are assessed through the analysis of conflict and postconflict very high spatial resolution Ikonos images using a pixel-level support vector machine (SVM) learning classification approach. Different input vectors of the supervised SVM classifier are tested in order to assess the discrimination power of structural and spectral image descriptors: the use of spectral information only with (a) the panchromatic images at time to and t(1), (b) the pan-sharpened images with the multi-spectral bands at time to and t(1), (c) the iteratively re-weighted multivariate alteration detection (IR-MAD) variates derived from dataset (b); the use of structural information only with image series resulting from the decomposition by the derivative of the morphological profile (DMP) of the panchromatic (d) and pan-sharpened (e) data; finally, the use of spectral and structural information simultaneously (f) and (g) by stacking up (a) and (d), and (b) and (e), respectively. The results show that the SVM performs better with feature vectors based on the simultaneous use of spectral and structural information rather than with those formed by the grey-level information or the DMPs only. Moreover, approach (f) requiring only two panchromatic data as input compete well with approaches (b), (e), and (g), which instead necessitate ten spectral channels as input.