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

Authors:
Jefersson Alex dos Santos;André Tavares da Silva;Ricardo da Silva Torres;Alexandre Xavier Falcão;Léo P. Magalhães;Rubens A. C. Lamparelli
Affiliations:
Institute of Computing, University of Campinas, Campinas, SP, Brazil;School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil;Institute of Computing, University of Campinas, Campinas, SP, Brazil;Institute of Computing, University of Campinas, Campinas, SP, Brazil;School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil;Center for Research in Agriculture, University of Campinas, Campinas, SP, Brazil
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Year:
2011

Citing 4
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Abstract

The use of remote sensing images as a source of information in agribusiness applications is very common. In those applications, it is fundamental to know how the space occupation is. However, identification 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 results. 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 objective is to aggregate user preferences to the search process. The proposed solution combines the Optimum-Path Forest (OPF) classifier with composite descriptors obtained by a Genetic Programming (GP) framework. The new approach has presented good results with respect to the identification of pasture and coffee crops, overcoming the results obtained by a recently proposed method and the traditional Maximimun Likelihood algorithm.