Where: The likelihood measure is the conjunct distribution, which in the case of observations i.d (X1; ... ; Xn). The Neural Networks Simulator searched for alternatives for the low of the Backpropagation pattern algorithm, whereas it is usually pointed as the main obstacle for a wider utilization in the images classification, the neural architecture (Figure 2). Further details about the algorithm may be found in et al., 1998), (Ribeiro and Centeno, 2001) and (KavzogluandMather, 2003) Figure 2: Neural networks simulator. The process consisted on training and classification of samples of the groups of similar variables for both classification methods, Maxver and Neural Networks, where the program classified the samples by one method and, then, by the other. In the classification by Maxver the samples collected by pixels are samples in which the numeric values of the sampled pixels are the data that compound the training file, using the Imagethreshold tool; these values enabled the control and the of the classification parameters. 1.4. Accuracy of the classified images The recognition of the patterns was carried out from 90 control points obtained with a global positioning system (GPS) in field, digitalized on the compound Likewise (Nepomuceno, 2000), the accuracy was carried out using the cross tabulation method, which consists on crossing the patterns of the classified images with the samples of the field verification classes. The main types of regions of interest selected from the land cover were: exposed land, presence of cocoa, water bodies, open forest, dense forest (Figure 3).
Where: The likelihood measure is the conjunct distribution, which in the case of observations i.d (X1; ... ; Xn). The Neural Networks Simulator searched for alternatives for the low of the Backpropagation pattern algorithm, whereas it is usually pointed as the main obstacle for a wider utilization in the images classification, the neural architecture (Figure 2). Further details about the algorithm may be found in et al., 1998), (Ribeiro and Centeno, 2001) and (KavzogluandMather, 2003) Figure 2: Neural networks simulator. The process consisted on training and classification of samples of the groups of similar variables for both classification methods, Maxver and Neural Networks, where the program classified the samples by one method and, then, by the other. In the classification by Maxver the samples collected by pixels are samples in which the numeric values of the sampled pixels are the data that compound the training file, using the Imagethreshold tool; these values enabled the control and the of the classification parameters. 1.4. Accuracy of the classified images The recognition of the patterns was carried out from 90 control points obtained with a global positioning system (GPS) in field, digitalized on the compound Likewise (Nepomuceno, 2000), the accuracy was carried out using the cross tabulation method, which consists on crossing the patterns of the classified images with the samples of the field verification classes. The main types of regions of interest selected from the land cover were: exposed land, presence of cocoa, water bodies, open forest, dense forest (Figure 3).
GT-39-20-20120620160022.docx