Interactive classification of remote sensing images by using optimum-path forest and genetic programming

Abstract. The use of remote sensing images as a source of informa-tion in agribusiness applications is very common. In those applications, it is fundamental to know how the space occupation is. However, iden-tification and recognition of crop regions in remote sensing images are not trivial tasks yet. Although there are automatic methods proposed to that, users very often prefer to identify regions manually. That happens because these methods are usually developed to solve specific problems, or, when they are of general purpose, they do not yield satisfying re-sults. This work presents a new interactive approach based on relevance feedback to recognize regions of remote sensing. Relevance feedback is a technique used in content-based image retrieval (CBIR) tasks. Its objec-tive is to aggregate user preferences to the search process. The proposed solution combines the Optimum-Path Forest (OPF) classifier with com-posite descriptors obtained by a Genetic Programming (GP) framework. The new approach has presented good results with respect to the identi-fication of pasture and coffee crops, overcoming the results obtained by a recently proposed method and the traditional Maximimun Likelihood algorithm