A hybrid approach to features extraction from multi-date ASTER imagery for land cover transformations

In this paper we propose a hybrid classification method, adopting the best features extraction strategy for each land cover class on multi-date ASTER data. To enable an effective comparison among images, Multivariate Alteration Detection (MAD) transformation was applied in the pre-processing phase, because of its high level of automation and reliability in the enhancement of change information among different images. Consequently, different features identification procedures, both spectral and object-based, were implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a post-classification comparison was performed on multi-date ASTER-derived land cover (LC) maps to evaluate the effects of change in the study area.