This dissertation compares three cascade multitemporal image classification methods based on: (1) Support Vector Machines (SVM), (2) Hidden Markov Models (HMM) and (3) Fuzzy Markov Chains (FMC). The robustness of the classification models is verified, by introducing outliers in the data set. Additionally, performance of each method is evaluated when the number of occurrences of each class transition is different in the training and in the testing set. The gain of exploiting a prior knowledge regarding the admissible transitions in each target site is also investigated. The experimental analysis is conducted over two data sets with different characteristics; specifically a pair of IKONOS images of Rio de Janeiro and a pair of LANDSAT7 images of Alcinópolis, Mato Grosso do Sul. This study has concluded that the overall accuracy of the three approaches are similar through all experiments. The superiority ofthe multitemporal approaches over the monotemporal counterparts was confirmed.