Improvement of the Accuracy on Image Classification Process through Incorporation of contextual Information
Leonardo Campos de Assis, Carlos Antonio Oliveira Vieira and Fabyano Fonseca Silva
Federal University of Vicosa Vicosa-MG, Brazil
Abstract:The present study used contextual information modelled through Bayesian Inference to improve image classification in urban areas, with objective to produce a vegetation Map of Belo Horizonte Municipal District (capital of Minas Gerais State - Brazil). Contextual information was inputted into the classification process through ancillary data and specialist knowledge about land cover types. Accuracy assessment shows that despite the amount of generated data (one image based on probability values to each vegetative category), the proposed method improved the image classification accuracy, being more efficient than a conventional Gaussian Maximum Likelihood estimator.
Keywords: contextual information; image processing; Bayesian Inference.