A study of classification techniques applied to CBERS satellite images

In this paper its presented classification methods for identify forests with araucaria angustifolia, using artificial intelligence and Fractal approach. Studies were made to perform experiments in which could be verified the suitable of ANNs for classification of CBERS satellite images. However, it was noticed in that classification a significant error exists. Then, it intends to continuity that study through the incorporation of new techniques of treatment of the images before the submission to Neural Networks training with the use of error minimization techniques. When applying the detection of borders in those images, it was noticed that those limits possesses, visibly, patterns that could be good as additional information for identification of a class. Therefore, it is supposed that those differences can be quantified by Fractal Dimension calculation, whose definition is going of encounter with the need of establishing patterns for those borders or limits. Fractal Dimension study verifies the adaptation of that technique to determine areas that the Neural Networks and the method Maximum Likelihood doesn’t get to distinguish.