A relevance feedback method based on genetic programming for classification of remote sensing images

This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learn...

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Bibliographic Details
Published in:Information sciences 2011-07, Vol.181 (13), p.2671-2684
Main Authors: dos Santos, J.A., Ferreira, C.D., Torres, R. da S., Gonçalves, M.A., Lamparelli, R.A.C.
Format: Article
Language:English
Subjects:
Classification
Content-based image retrieval
Genetic programming
Genetics
Image classification
Programming
Region descriptors
Relevance feedback
Remote sensing
Remote sensing image classification
Surface layer
Texture
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Summary:This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learning user preferences and combining image region descriptors that encode spectral and texture properties. Experiments demonstrate that the proposed method is effective for image classification tasks and outperforms the traditional MaxVer method.
ISSN:0020-0255
DOI:10.1016/j.ins.2010.02.003