Anais do XX Simpósio Brasileiro de Sensoriamento Remoto

A challenge for the use of medium spatial resolution imagery for change detection consists of the reduced availability of ground reference data for previous dates. We compared the accuracy of invariant area sets, generated by three methods (Iteratively Reweighted Multivariate Alteration Detection, Change Vector Analysis and Spectral Gradient Difference) for two periods (2017-2011 and 2011-2006). The classification of the Landsat-5 TM image of 2006 was performed using as training data the sets of points indicated as invariant in the binary maps resulted from the three methods. Overall accuracy for seven land-use classes was greater (80,5% and 80,2%) when using training areas achieved by CVA and SGD, respectively than IR-MAD (76%). Were obtained accuracies greater than 80% for the forest class. The results stress that the combination of the IR-MAD and SGD is preferable since the CVA is more time consuming due to the subjective application of thresholds.