The objective of this paper is twofold: first, to presents a generic approach for the analysis of Radarsat-1
multitemporal data and, second, to presents a multi classifier schema for the classification of multitemporal
images. The general approach consists of preprocessing step and classification. In the preprocessing stage, the
images are calibrated and registered and then temporally filtered. The resulted multitemporally filtered images
are subsequently used as the input images in the classification step. The first step in a classifier design is to
pick up the most informative features from a series of multitemporal SAR images. Most of the feature selection
algorithms seek only one set of features that distinguish among all the classes simultaneously and hence a limited
amount of classification accuracy. In this paper, a class-based feature selection (CBFS) was proposed. In this
schema, instead of using feature selection for the whole classes, the features are selected for each class separately.
The selection is based on the calculation of JM distance of each class from the rest of classes. Afterwards,
a maximum likelihood classifier is trained on each of the selected feature subsets. Finally, the outputs of the
classifiers are combined through a combination mechanism. Experiments are performed on a set of 34 Radarsat-1
images acquired from August 1996 to February 2007. A set of 9 classes in a forest area are used in this study.
Classification results confirm the effectiveness of the proposed approach compared with the case of single feature
selection. Moreover, the proposed process is generic and hence is applicable in different mapping purposes for
which a multitemporal set of SAR images are available.